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We are looking for the bestAbout Us42dot은 소프트웨어와 AI로 모빌리티 문제를 해결하기 위해 노력하는 모빌리티 AI 기업입니다. 현대자동차그룹 글로벌 소프트웨어 센터로서, 42dot은 소프트웨어 정의 차량 개발을 선도하며 미래 모빌리티를 개척하고 있습니다.우리는 안전을 최우선으로 하고 사용자 중심적인 소프트웨어 정의 차량 기술을 개발하여, 스마트폰처럼 지속적인 업데이트를 통해 최신 성능을 제공합니다. 소프트웨어와 AI 기술을 발전시켜, 42dot은 모든 것이 연결되고 자율적으로 움직이는 자율 관리형 도시 교통 운영 체제를 통해 새로운 세상을 그려 나가고 있습니다.Intelligence Service 팀은 도메인별 컬렉션 검색 서비스를 설계, 서빙, 운영하고, 검색 품질과 랭킹을 끌어올려 실제 서비스 품질로 연결합니다. 우리는 검색 아키텍처 설계, 또는 검색 품질·랭킹·추천 중 하나 이상의 영역에서 강점을 가진 시니어 백엔드 엔지니어를 찾고 있습니다. 초기 구조 설계부터 운영 품질 개선까지 주도하며, Intelligence Service의 검색·품질 플랫폼을 함께 만들어갈 분을 기대합니다.Responsibilities검색 서비스 구축 및 운영: 뉴스, 장소, 증권, 스포츠, 뮤직, 영화 등 다양한 도메인별 컬렉션 검색 서비스의 백엔드 시스템을 설계, 개발 및 운영합니다.아키텍처 설계 및 표준화: OpenSearch / MongoDB Atlas 기반의 검색 시스템과 인덱싱, retrieval 구조를 설계하며, 신규 컬렉션을 빠르게 확장할 수 있도록 검색 아키텍처를 표준화합니다.검색 품질 분석 및 지표 운영: 운영 로그와 nDCG, recall, MRR, CTR 등 온/오프라인 품질 지표를 기반으로 검색 정확도, latency, failure case를 분석하고 개선합니다.랭킹 모델 및 추천 로직 고도화: 데이터 특성에 맞는 ranking model, reranker, embedding, semantic search 및 추천 로직을 개발하고, top 1~3 정확도가 중요한 컬렉션을 정밀 튜닝합니다.프로덕션 운영 환경 구축: API contract, cache, config registry, admin API 등 안정적인 서비스 운영에 필요한 백엔드 기반을 탄탄하게 구축합니다.기술 리딩 및 문제 해결: 팀 내 설계 리뷰와 기술 의사결정을 리딩하며, 복잡한 문제를 재사용 가능한 시스템 개선으로 연결합니다.Qualifications백엔드 서비스 설계, 개발, 운영 경력 7년 이상이신 분Python, Java, Kotlin 중 하나 이상의 언어를 능숙하게 사용하시는 분production traffic을 받는 API/backend service를 설계하고 운영해 보신 분검색, 추천, 랭킹, 데이터 플랫폼 중 하나 이상의 도메인에서 실제 서비스 개발 경험이 있으신 분로그와 지표를 기반으로 검색/품질 문제의 원인을 분석하고 개선해 보신 분초기 설계, 구조화, 표준화, 운영 안정성 개선을 주도해 보신 분Preferred QualificationsOpenSearch, Elasticsearch, MongoDB Atlas Search 등 검색 시스템 개발 경험Semantic Search, Embedding, Reranker, Vector Search, Hybrid Search(text + vector + rankFusion) 경험ranking model, recommendation system, personalization 개발 경험nDCG, MRR, recall, CTR 등 검색/추천 품질 지표 설계 및 운영 경험AWS, Kubernetes, Helm 기반 서비스 운영 경험차량, 음성 비서, 다국어/다지역 서비스 경험기술 리딩, 설계 리뷰, 주니어 멘토링 경험Interview Process서류전형 - 코딩테스트 - 화상면접 (1시간 내외) - 대면 혹은 화상면접 (3시간 내외) - 최종합격전형절차는 직무별로 다르게 운영될 수 있으며, 일정 및 상황에 따라 변동될 수 있습니다.전형일정 및 결과는 지원서에 등록하신 이메일로 개별 안내드립니다.Additional Information이력서 제출 시 주민등록번호, 가족관계, 혼인 여부, 연봉, 사진, 신체조건, 출신 지역 등 채용절차법상 요구 금지된 정보는 제외 부탁드립니다.모든 제출 파일은 30MB 이하의 PDF 양식으로 업로드를 부탁드립니다. (이력서 업로드 중 문제가 발생한다면 지원하시고자 하는 포지션의 URL과 함께 이력서를 recruit@42dot.ai으로 전송 부탁드립니다.)인터뷰 프로세스 종료 후 지원자의 동의하에 평판조회가 진행될 수 있습니다.국가보훈대상자 및 취업보호 대상자는 관계법령에 따라 우대합니다.장애인 고용 촉진 및 직업재활법에 따라 장애인 등록증 소지자를 우대합니다.42dot은 의뢰하지 않은 서치펌의 이력서를 받지 않으며, 요청하지 않은 이력서에 대해 수수료를 지불하지 않습니다.3개월의 수습기간이 적용될 수 있습니다.※ 지원 전 아래 내용을 꼭 확인해 주세요.42dot이 일하는 방식, 42dot Way 보러가기 →42dot만의 업무몰입 프로그램, Employee Engagement Program 보러가기 →

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About the Company:Netomi is the leading agentic AI platform for enterprise customer experience. We work with the largest global brands like Delta Airlines, MetLife, MGM, United, and others to enable agentic automation at scale across the entire customer journey. Our no-code platform delivers the fastest time to market, lowest total cost of ownership, and simple, scalable management of AI agents for any CX use case. Backed by WndrCo, Y Combinator, and Index Ventures, we help enterprises drive efficiency, lower costs, and deliver higher quality customer experiences. Want to be part of the AI revolution and transform how the world’s largest global brands do business? Join us!About the role We are looking for an Agentic Solution Engineer to build and scale agentic workflows and tools using Netomi’s no-code platform. This role requires strong software engineering fundamentals combined with prompt engineering and API-driven automation to power reliable, autonomous AI agents.Responsibilities Design and write high-quality prompts for LLM-based agents (GPT-class models preferred). Build agentic tools and workflows using Netomi’s no-code platform (similar to Workato, Zapier, n8n). Integrate external and internal APIs, including authentication, data mapping, and error handling. Implement unit tests, debug issues, and ensure reliability of agent workflows. Apply engineering best practices and design patterns such as retries, timeouts, idempotency, and lazy loading. Optimize agents for performance, cost, and fault tolerance. Collaborate with Product, QA, and Delivery teams to ship production-grade agentic solutions. Requirement 5-7 years of experience as a Software Development Engineer or similar role. Strong understanding of prompt engineering and LLM behavior. Strong ability to partner with customers and business stakeholders to understand requirements and translate them into actionable solution designs. Hands-on experience with REST APIs, webhooks, and JSON-based integrations. Solid experience in unit testing, debugging, and troubleshooting. Knowledge of resilient system design patterns and non-happy path handling. Nice to Have Experience with no-code/low-code automation platforms. Exposure to agentic AI, conversational AI, or workflow orchestration systems. Familiarity with observability and monitoring in production systems. Netomi is an equal opportunity employer committed to diversity in the workplace. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, disability, veteran status, and other protected characteristics.

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About the Company:Netomi is the leading agentic AI platform for enterprise customer experience. We work with the largest global brands like Delta Airlines, MetLife, MGM, United, and others to enable agentic automation at scale across the entire customer journey. Our no-code platform delivers the fastest time to market, lowest total cost of ownership, and simple, scalable management of AI agents for any CX use case. Backed by WndrCo, Y Combinator, and Index Ventures, we help enterprises drive efficiency, lower costs, and deliver higher quality customer experiences. Want to be part of the AI revolution and transform how the world’s largest global brands do business? Join us!About the Role As an Agentic Product Analyst at Netomi, you will be the strategic and technical owner responsible for designing, architecting, and deploying large-scale Agentic AI solutions for enterprise customers. You will translate complex business processes into AI-driven workflows, define integration blueprints, and guide customers through their transformation to autonomous AI agents.  You will work closely with Customers, CSM, Integration Engineers, QA, Product, and Engineering to ensure end-to-end success of every deployment.Responsibilities Lead discovery sessions with enterprise customers to understand business processes, operations, workflows, KPIs, and constraints. Identify automation opportunities and design agentic orchestration strategies using Netomi’s AI platform. Build detailed solution blueprints — workflows, data exchanges, escalation logic, guardrails, analytics, and agent lifecycle design. Define end-to-end Agentic AI architectures, covering intents, actions, tools, integration points, and decision logic. Work with customer technical teams to map integration dependencies (APIs, events, SSO, CRMs, ticketing systems, internal tools). Own the creation of integration design documents and support Integration Engineers during implementation. Ensure agent workflows comply with enterprise standards (security, reliability, audit, governance). Collaborate with Product & Engineering to translate customer requirements into features or enhancements. Serve as the product owner during deployment — driving priorities, clarifications, acceptance criteria, and workflow evolution. Validate solution behavior end-to-end with QA and guide test plans, scenario tests, and success criteria. Conduct user-experience reviews of agent outputs and behavior tuning. Act as a trusted advisor to customer stakeholders (CX, Ops, Digital Transformation, Engineering). Present architectural recommendations, best practices, and roadmap suggestions. Train customer teams on agentic workflows, governance, and performance improvement strategies. Ensure projects are delivered on time with high quality, low rework, and measurable impact. Drive continuous improvement through playbooks, reusable workflow templates, and integration patterns. Maintain deep expertise in agentic AI, LLMs, workflow orchestration, and enterprise systems. Requirements 5-7 years in Solutions Architecture / Implementation Consulting / Product Consulting / Workflow Design for enterprise SaaS or AI platforms. Experience designing complex workflows and integrations across enterprise systems. Ability to gather requirements, translate them into technical specifications, and drive execution. Customer-facing consulting experience with strong communication & facilitation skills. Understanding of REST APIs, event-driven systems, data flows, and enterprise integrations. Experience working cross-functionally with CSM, engineering, product, and QA teams. Nice-to-Have Background in Conversational AI, Agentic AI, RPA, Automation, CRM/Ticketing, or LLM-based products. Hands-on experience defining business logic, decision trees, or automated workflows. Experience with enterprises such as airlines, BFSI, telecom, retail, or large-scale customer support environments. Netomi is an equal opportunity employer committed to diversity in the workplace. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, disability, veteran status, and other protected characteristics.

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About Level AI Level AI is on a mission to turn every customer interaction into a strategic advantage. Our AI-native platform helps enterprises transform contact centers from cost centers into engines of customer intelligence, operational efficiency, and business growth. By combining advanced AI with deep domain understanding of customer experience, Level AI empowers teams to unlock actionable insights, automate workflows, and deliver more consistent, higher-quality support across the customer journey. Headquartered in Mountain View, California, Level AI is a Series C company backed by leading investors including Battery Ventures and ENIAC. Our platform leverages Large Language Models and Custom Small Language Models (SLMs) to power AI Agents across the entire CX journey—customer-facing agents, agent-assist, and backend automation—along with deep conversation analytics for QA, coaching, and insights. About the role As a Senior Engineer – AI Agents, you will play a critical role in building the scalable backend systems that power Level AI’s next-generation AI Agents. These systems operate in real-time, high-volume enterprise environments and are central to delivering intelligent, production-grade AI experiences. You will work at the intersection of distributed systems, cloud infrastructure, and AI-powered applications—bringing agentic AI capabilities into production at scale.What you’ll get to do at Level AI (and more as we grow together): Design and build scalable backend systems powering AI Agents that operate in real-time enterprise environments Develop agent orchestration frameworks (multi-step reasoning, tool usage, decisioning workflows) Build systems for agent memory, context management, and state persistence across interactions Architect low-latency inference pipelines integrating LLMs, SLMs, and external tools/services Implement evaluation (evals) frameworks to measure agent performance, accuracy, and reliability Enable continuous improvement loops (feedback → retraining → deployment) for AI agents in production Design and manage event-driven, asynchronous workflows for complex agent tasks Optimize systems for high throughput, low latency, and cost-efficient inference at scale Build and maintain robust APIs and service layers (REST / gRPC) for agent capabilities Partner closely with Applied AI / ML teams to productionize models and agent behaviours. Collaborate with Product and Solutions teams to translate real customer workflows into agentic systems Drive best practices in observability, monitoring, safety, and guardrails for AI systems Contribute to architecture decisions for scaling multi-tenant, enterprise-grade AI platforms    We'll love to explore more about you if you have:                         5+ years of experience in backend engineering, distributed systems, or platform engineering Strong experience building high-scale, production-grade backend systems Experience designing systems for real-time processing, streaming, or event-driven architectures Strong understanding of API design (REST, gRPC) and microservices architectures Experience with databases (SQL + NoSQL) and data modeling for high-scale systems Hands-on experience with Docker, Kubernetes, and cloud platforms (AWS/GCP/Azure) Strong fundamentals in system design, concurrency, and performance optimization Strong Plus (Agent / AI focus): Experience working with LLMs, conversational AI, or AI-powered products in production Familiarity with agent frameworks, tool calling, or multi-step reasoning systems Experience building or integrating RAG pipelines, vector databases, or retrieval systems Exposure to evaluation systems (offline/online evals, A/B testing for AI systems) Understanding of prompting strategies, context windows, and model behavior optimization Experience with real-time decisioning systems or workflow orchestration engines Perks & Benefits Competitive compensation with performance-based upside Flexible vacation policy Health insurance coverage Work with a globally distributed, high-impact team Opportunity to build cutting-edge AI products at scale Regular team offsites and in-person collaboration  To learn more visit : https://thelevel.ai/ Funding : https://www.crunchbase.com/organization/level-ai LinkedIn : https://www.linkedin.com/company/level-ai/ Our AI platform : https://www.youtube.com/watch?v=g06q2V_kb-s

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About FluidstackWe exist to make humanity more free. For most of human history, you farmed or you starved. Technology gave people more time for the things they wanted to do, instead of things they had to do. Powerful AI will be the biggest lever for human choice we've ever built - but only if models are aligned with what humanity actually wants. There are groups building AI who don't share these goals. Whoever deploys frontier compute infrastructure fastest will decide whether AI expands human freedom or shrinks it. We're singularly focused on delivering 10 to 100s of GWs of compute faster than anyone else, rethinking every layer of the stack. We acquire power, design and build data centers, and operate them - with teams spanning hardware and software. Speed and scale are our key differentiators. Come be a part of building civilization-scale infrastructure for AI. We hire people who care deeply about this problem space. If that is you, please apply!How We OperateHigh ownership. Full autonomy. Own things end to end often taking on scope outside your core role without being asked to get things done.Velocity. We drive everything forward as fast as possible.First principles. Challenge every assumption. Zero analogy thinking, no egos, the best idea wins.Love of the game. The frontier of AI is the most interesting problem of our time. We put in long hours at high intensity to push the frontier forward. The Software Engineering TeamExamples of key exciting problems the team is working on:We're building the pipeline that processes thousand-page vendor unstructured material against our own DC design to automate the generation of an end to end site design and schedule.We're building the live model of a AI infrastructure project’s entire delivery schedule, durable workflows handing off across every team, to replace static Gantt charts with a plan that updates itself as reality changes.We're building the agent platform that wraps AI in real authorization, audit, and guardrails, to let agents accelerate the development of infrastructure instead of just advising report on it. Role ScopeBuild core primitives end to end. Entity ownership, audit, authorization, orchestration. You make the right thing the default and the wrong thing hard.Own the domain model. Turn Fluidstack's world of power, datacenters, and chips into composable entities that still hold up years from now.Define interactions with the outside. Interface with vendor systems and ingest the formats our domain actually speaks: KMZ, BIM, Revit, vendor docs.Make AI agents first-class operators of our systems. Give them the tools, guardrails, and audit trails to act safely, not just advise.What We're Looking ForThe below is a starting point. We always make space for exceptional people, so if you don't fit this role exactly, tell us where you would.You move toward ambiguity, not away from it. You walk into the fog, build the map, and explain it to everyone else.You learn at a steep slope. You reach real competence in an unfamiliar domain fast, whether it's a workflow engine, an authz model, or a geospatial format. We value this over existing expertise.You have taste in abstraction. You see the one primitive hiding under five janky one-offs, and you know when not to abstract.You model domains with rigor earned the hard way. You've gotten it wrong, felt the pain, and never forgot it.You pull structure out of mess. You've done real work turning messy, unstructured data into something usable, in ML, data engineering, or research.You're fluent with AI tooling. LLM APIs, MCP servers, and agentic frameworks, and you drive Claude Code, Cursor, or similar every day.You've shipped production code that other people depend on, and you're comfortable in any language using AI coding tools.Bonus: MLE work, especially extraction and synthesis from unstructured data. Workflow and orchestration engines (Temporal, Cadence, Airflow). Geospatial (KMZ/GIS) or BIM (Revit, IFC). Authorization and policy engines (OPA, Zanzibar-style, RBAC/ABAC). Audit or event-sourcing systems. Go, TypeScript, and Postgres. Salary & BenefitsCompetitive total compensation package (salary + equity).Retirement or pension plan, in line with local norms.Health, dental, and vision insurance.Generous PTO policy, in line with local norms.The base salary range for this position is $150,000 - $250,000 per year, depending on experience, skills, qualifications, and location. This range represents our good faith estimate of the compensation for this role at the time of posting. Total compensation may also include equity in the form of stock options.We are committed to pay equity and transparency.Fluidstack is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans’ status, or any other characteristic protected by law. Fluidstack will consider for employment qualified applicants with arrest and conviction records pursuant to applicable law.You will receive a confirmation email once your application has successfully been accepted. If there is an error with your submission and you did not receive a confirmation email, please email careers@fluidstack.io with your resume/CV, the role you've applied for, and the date you submitted your application-- someone from our recruiting team will be in touch.

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About Us: At Fireworks, we’re building the future of generative AI infrastructure. Our platform delivers the highest-quality models with the fastest and most scalable inference in the industry. We’ve been independently benchmarked as the leader in LLM inference speed and are driving cutting-edge innovation through projects like our own function calling and multimodal models. Fireworks is a Series C company valued at $4 billion and backed by top investors including Benchmark, Sequoia, Lightspeed, Index, and Evantic. We’re an ambitious, collaborative team of builders, founded by veterans of Meta PyTorch and Google Vertex AI.In the last few months alone we launched Fireworks Training, partnered with Microsoft Azure Foundry, and published research straight from our production systems. A few examples of what that looks like in practice: Frontier RL is cheaper than the mega-cluster narrative suggests: we ran cross-region rollouts using 98% sparse weight deltas and published what we learned. (blog) Open source agents with frontier advisors: matching frontier performance through training and harness engineering. (blog) The fine-tuning bottleneck is not the algorithm: integration friction and iteration speed are what actually stall teams; we documented the patterns across dozens of customer engagements. (blog) The Role: AI Field Engineers at Fireworks are the technical tip of the spear. You embed with our most ambitious customers and technology partners to turn complex AI problems into production systems, fast. The role sits at the intersection of engineering, product, and customer delivery. You are hands-on-keyboard building POCs, MVPs, and production integrations, while also holding your own in executive-level conversations about architecture, strategy, and business outcomes. You spend most of your time building. You ship code, run benchmarks, debug production issues, and architect deployments. But you also lead discovery conversations, align stakeholders, and translate customer pain points into product improvements that compress the feedback loop from field to roadmap. This is a role for engineers who are comfortable on-site with customers, building the relationships and trust that happen in person, not just over a call. The Segment As a Field Engineer in the AI Native segment you will work with the most innovative AI-native companies building at the frontier, where GenAI is the core product, not a feature, and where Fireworks is the platform they depend on to ship and scale it. These engagements move fast with fewer stakeholders, so you will spend more time in the code and iterate alongside their engineering teams, while still holding executive-level conversations on architecture and strategy. You will embed deeply with a small set of high-velocity accounts where the quality of your engineering is the relationship. What You'll Work On Technical Delivery and Deployment Build end-to-end POCs and MVPs alongside customer engineering teams, working inside their codebases, infrastructure, and constraints. For customers whose core product is built on GenAI, architect the inference foundations that capability depends on, and size deployments so they can scale in their market without infrastructure becoming the bottleneck. Run load tests and establish latency, throughput, and cost baselines against realistic customer traffic profiles, and tune deployments to hit those targets Deploy and validate new model families on inference frameworks (vLLM, SGLang), determining optimal shapes, quantization configs, and serving patterns across workloads. Model Strategy and Fine-Tuning Guide customers on model selection, fine-tuning strategy (SFT, DPO, RFT), and evaluation methodology. Build and run fine-tuning pipelines directly with customers, navigating trade-offs between model families, compute cost, and quality targets. Design and implement evaluation frameworks that measure production-quality metrics, not just benchmark scores. Customer Engagement and Stakeholder Management Many of our customers exist because of GenAI. Help them bake frontier model capabilities into their core offering and turn that into a durable competitive edge. Lead structured discovery conversations to unpack customer pain points, constraints, and success criteria before proposing solutions. Own the technical relationship from first engagement through production deployment. Embed with their engineering team as a peer, your credibility comes from what you build alongside them. Spend time on-site with customers. Build trust and momentum in person, embedding with their teams where the work happens. Product Feedback and Platform Improvement Identify recurring customer pain points and translate them into concrete product proposals, working directly with engineering and product to ship fixes and features. Codify repeatable deployment patterns and contribute them back to internal tooling, documentation, and the platform itself. Feed customer signals (deployment patterns, failure modes, feature gaps) back into the product roadmap with specificity and urgency. What We're Looking For: Minimum Qualifications 5+ years in a hands-on, customer-facing technical role: Forward Deployed Engineer, Applied AI Engineer, Solutions Architect, ML Engineer with field exposure, or technical founder. Demonstrated ability to build production software with customers, not just advise on it. You have shipped code running in someone else's production environment. Strong Python skills. Comfortable reading, writing, and debugging production code. Familiarity with Kubernetes and infrastructure engineering. Working knowledge of the LLM stack: inference trade-offs, model serving, fine-tuning workflows (SFT at minimum; DPO/RFT a strong plus). Experience with cloud infrastructure (AWS, Azure, GCP) and deploying models on GPU infrastructure. Exceptional communication: able to run a sharp discovery call, present to a VP, and debug a latency issue with an ML engineer in the same afternoon. Experience building or integrating agentic systems, tool-use chains, or AI-native developer toolchains. Preferred Qualifications 10+ years in technical field or engineering roles. Experience with inference serving frameworks (vLLM, SGLang, TensorRT-LLM) and tuning deployments for real workloads. Prior experience at a company with a forward-deployed or embedded engineering model (Palantir, Scale AI, Anthropic, OpenAI, BCG X, McKinsey Quantum Black, AI Native startups with FDE motions). Prior experience as a technical founder or early engineer at an AI-native company is a strong signal. Track record taking GenAI POCs from prototype to production-scale deployments. Experience with hyperscaler AI platforms (Azure AI Foundry, AWS Bedrock/SageMaker, GCP Vertex). Total compensation for this role also includes meaningful equity in a fast-growing startup, along with a competitive salary and comprehensive benefits package. Base salary is determined by a range of factors including individual qualifications, experience, skills, interview performance, market data, and work location. The listed salary range is intended as a guideline and may be adjusted.On Target Earnings (Plus Equity)$200,000—$260,000 USDWhy Fireworks AI? Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving. Build What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally. Ownership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI—no bureaucracy, just results. Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation. Fireworks AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.

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About Us: At Fireworks, we’re building the future of generative AI infrastructure. Our platform delivers the highest-quality models with the fastest and most scalable inference in the industry. We’ve been independently benchmarked as the leader in LLM inference speed and are driving cutting-edge innovation through projects like our own function calling and multimodal models. Fireworks is a Series C company valued at $4 billion and backed by top investors including Benchmark, Sequoia, Lightspeed, Index, and Evantic. We’re an ambitious, collaborative team of builders, founded by veterans of Meta PyTorch and Google Vertex AI.In the last few months alone we launched Fireworks Training, partnered with Microsoft Azure Foundry, and published research straight from our production systems. A few examples of what that looks like in practice: Frontier RL is cheaper than the mega-cluster narrative suggests: we ran cross-region rollouts using 98% sparse weight deltas and published what we learned. (blog) Open source agents with frontier advisors: matching frontier performance through training and harness engineering. (blog) The fine-tuning bottleneck is not the algorithm: integration friction and iteration speed are what actually stall teams; we documented the patterns across dozens of customer engagements. (blog) The Role: AI Field Engineers at Fireworks are the technical tip of the spear. You embed with our most ambitious customers and technology partners to turn complex AI problems into production systems, fast. The role sits at the intersection of engineering, product, and customer delivery. You are hands-on-keyboard building POCs, MVPs, and production integrations, while also holding your own in executive-level conversations about architecture, strategy, and business outcomes. You spend most of your time building. You ship code, run benchmarks, debug production issues, and architect deployments. But you also lead discovery conversations, align stakeholders, and translate customer pain points into product improvements that compress the feedback loop from field to roadmap. This is a role for engineers who are comfortable on-site with customers, building the relationships and trust that happen in person, not just over a call. The Segment As a Field Engineer in the AI Native segment you will work with the most innovative AI-native companies building at the frontier, where GenAI is the core product, not a feature, and where Fireworks is the platform they depend on to ship and scale it. These engagements move fast with fewer stakeholders, so you will spend more time in the code and iterate alongside their engineering teams, while still holding executive-level conversations on architecture and strategy. You will embed deeply with a small set of high-velocity accounts where the quality of your engineering is the relationship. What You'll Work On Technical Delivery and Deployment Build end-to-end POCs and MVPs alongside customer engineering teams, working inside their codebases, infrastructure, and constraints. For customers whose core product is built on GenAI, architect the inference foundations that capability depends on, and size deployments so they can scale in their market without infrastructure becoming the bottleneck. Run load tests and establish latency, throughput, and cost baselines against realistic customer traffic profiles, and tune deployments to hit those targets Deploy and validate new model families on inference frameworks (vLLM, SGLang), determining optimal shapes, quantization configs, and serving patterns across workloads. Model Strategy and Fine-Tuning Guide customers on model selection, fine-tuning strategy (SFT, DPO, RFT), and evaluation methodology. Build and run fine-tuning pipelines directly with customers, navigating trade-offs between model families, compute cost, and quality targets. Design and implement evaluation frameworks that measure production-quality metrics, not just benchmark scores. Customer Engagement and Stakeholder Management Many of our customers exist because of GenAI. Help them bake frontier model capabilities into their core offering and turn that into a durable competitive edge. Lead structured discovery conversations to unpack customer pain points, constraints, and success criteria before proposing solutions. Own the technical relationship from first engagement through production deployment. Embed with their engineering team as a peer, your credibility comes from what you build alongside them. Spend time on-site with customers. Build trust and momentum in person, embedding with their teams where the work happens. Product Feedback and Platform Improvement Identify recurring customer pain points and translate them into concrete product proposals, working directly with engineering and product to ship fixes and features. Codify repeatable deployment patterns and contribute them back to internal tooling, documentation, and the platform itself. Feed customer signals (deployment patterns, failure modes, feature gaps) back into the product roadmap with specificity and urgency. What We're Looking For: Minimum Qualifications 5+ years in a hands-on, customer-facing technical role: Forward Deployed Engineer, Applied AI Engineer, Solutions Architect, ML Engineer with field exposure, or technical founder. Demonstrated ability to build production software with customers, not just advise on it. You have shipped code running in someone else's production environment. Strong Python skills. Comfortable reading, writing, and debugging production code. Familiarity with Kubernetes and infrastructure engineering. Working knowledge of the LLM stack: inference trade-offs, model serving, fine-tuning workflows (SFT at minimum; DPO/RFT a strong plus). Experience with cloud infrastructure (AWS, Azure, GCP) and deploying models on GPU infrastructure. Exceptional communication: able to run a sharp discovery call, present to a VP, and debug a latency issue with an ML engineer in the same afternoon. Experience building or integrating agentic systems, tool-use chains, or AI-native developer toolchains. Preferred Qualifications 10+ years in technical field or engineering roles. Experience with inference serving frameworks (vLLM, SGLang, TensorRT-LLM) and tuning deployments for real workloads. Prior experience at a company with a forward-deployed or embedded engineering model (Palantir, Scale AI, Anthropic, OpenAI, BCG X, McKinsey Quantum Black, AI Native startups with FDE motions). Prior experience as a technical founder or early engineer at an AI-native company is a strong signal. Track record taking GenAI POCs from prototype to production-scale deployments. Experience with hyperscaler AI platforms (Azure AI Foundry, AWS Bedrock/SageMaker, GCP Vertex). Total compensation for this role also includes meaningful equity in a fast-growing startup, along with a competitive salary and comprehensive benefits package. Base salary is determined by a range of factors including individual qualifications, experience, skills, interview performance, market data, and work location. The listed salary range is intended as a guideline and may be adjusted.On Target Earnings (Plus Equity)$200,000—$260,000 USDWhy Fireworks AI? Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving. Build What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally. Ownership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI—no bureaucracy, just results. Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation. Fireworks AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.

false

About Us: At Fireworks, we’re building the future of generative AI infrastructure. Our platform delivers the highest-quality models with the fastest and most scalable inference in the industry. We’ve been independently benchmarked as the leader in LLM inference speed and are driving cutting-edge innovation through projects like our own function calling and multimodal models. Fireworks is a Series C company valued at $4 billion and backed by top investors including Benchmark, Sequoia, Lightspeed, Index, and Evantic. We’re an ambitious, collaborative team of builders, founded by veterans of Meta PyTorch and Google Vertex AI.In the last few months alone we launched Fireworks Training, partnered with Microsoft Azure Foundry, and published research straight from our production systems. A few examples of what that looks like in practice: Frontier RL is cheaper than the mega-cluster narrative suggests: we ran cross-region rollouts using 98% sparse weight deltas and published what we learned. (blog) Open source agents with frontier advisors: matching frontier performance through training and harness engineering. (blog) The fine-tuning bottleneck is not the algorithm: integration friction and iteration speed are what actually stall teams; we documented the patterns across dozens of customer engagements. (blog) The Role: AI Field Engineers at Fireworks are the technical tip of the spear. You embed with our most ambitious customers and technology partners to turn complex AI problems into production systems, fast. The role sits at the intersection of engineering, product, and customer delivery. You are hands-on-keyboard building POCs, MVPs, and production integrations, while also holding your own in executive-level conversations about architecture, strategy, and business outcomes. You spend most of your time building. You ship code, run benchmarks, debug production issues, and architect deployments. But you also lead discovery conversations, align stakeholders, and translate customer pain points into product improvements that compress the feedback loop from field to roadmap. This is a role for engineers who are comfortable on-site with customers, building the relationships and trust that happen in person, not just over a call. The Segment As a Field Engineer in the AI Native segment you will work with the most innovative AI-native companies building at the frontier, where GenAI is the core product, not a feature, and where Fireworks is the platform they depend on to ship and scale it. These engagements move fast with fewer stakeholders, so you will spend more time in the code and iterate alongside their engineering teams, while still holding executive-level conversations on architecture and strategy. You will embed deeply with a small set of high-velocity accounts where the quality of your engineering is the relationship. What You'll Work On Technical Delivery and Deployment Build end-to-end POCs and MVPs alongside customer engineering teams, working inside their codebases, infrastructure, and constraints. For customers whose core product is built on GenAI, architect the inference foundations that capability depends on, and size deployments so they can scale in their market without infrastructure becoming the bottleneck. Run load tests and establish latency, throughput, and cost baselines against realistic customer traffic profiles, and tune deployments to hit those targets Deploy and validate new model families on inference frameworks (vLLM, SGLang), determining optimal shapes, quantization configs, and serving patterns across workloads. Model Strategy and Fine-Tuning Guide customers on model selection, fine-tuning strategy (SFT, DPO, RFT), and evaluation methodology. Build and run fine-tuning pipelines directly with customers, navigating trade-offs between model families, compute cost, and quality targets. Design and implement evaluation frameworks that measure production-quality metrics, not just benchmark scores. Customer Engagement and Stakeholder Management Many of our customers exist because of GenAI. Help them bake frontier model capabilities into their core offering and turn that into a durable competitive edge. Lead structured discovery conversations to unpack customer pain points, constraints, and success criteria before proposing solutions. Own the technical relationship from first engagement through production deployment. Embed with their engineering team as a peer, your credibility comes from what you build alongside them. Spend time on-site with customers. Build trust and momentum in person, embedding with their teams where the work happens. Product Feedback and Platform Improvement Identify recurring customer pain points and translate them into concrete product proposals, working directly with engineering and product to ship fixes and features. Codify repeatable deployment patterns and contribute them back to internal tooling, documentation, and the platform itself. Feed customer signals (deployment patterns, failure modes, feature gaps) back into the product roadmap with specificity and urgency. What We're Looking For: Minimum Qualifications 5+ years in a hands-on, customer-facing technical role: Forward Deployed Engineer, Applied AI Engineer, Solutions Architect, ML Engineer with field exposure, or technical founder. Demonstrated ability to build production software with customers, not just advise on it. You have shipped code running in someone else's production environment. Strong Python skills. Comfortable reading, writing, and debugging production code. Familiarity with Kubernetes and infrastructure engineering. Working knowledge of the LLM stack: inference trade-offs, model serving, fine-tuning workflows (SFT at minimum; DPO/RFT a strong plus). Experience with cloud infrastructure (AWS, Azure, GCP) and deploying models on GPU infrastructure. Exceptional communication: able to run a sharp discovery call, present to a VP, and debug a latency issue with an ML engineer in the same afternoon. Experience building or integrating agentic systems, tool-use chains, or AI-native developer toolchains. Preferred Qualifications 10+ years in technical field or engineering roles. Experience with inference serving frameworks (vLLM, SGLang, TensorRT-LLM) and tuning deployments for real workloads. Prior experience at a company with a forward-deployed or embedded engineering model (Palantir, Scale AI, Anthropic, OpenAI, BCG X, McKinsey Quantum Black, AI Native startups with FDE motions). Prior experience as a technical founder or early engineer at an AI-native company is a strong signal. Track record taking GenAI POCs from prototype to production-scale deployments. Experience with hyperscaler AI platforms (Azure AI Foundry, AWS Bedrock/SageMaker, GCP Vertex). Total compensation for this role also includes meaningful equity in a fast-growing startup, along with a competitive salary and comprehensive benefits package. Base salary is determined by a range of factors including individual qualifications, experience, skills, interview performance, market data, and work location. The listed salary range is intended as a guideline and may be adjusted.On Target Earnings (Plus Equity)$200,000—$260,000 USDWhy Fireworks AI? Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving. Build What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally. Ownership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI—no bureaucracy, just results. Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation. Fireworks AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.

false

About Us: At Fireworks, we’re building the future of generative AI infrastructure. Our platform delivers the highest-quality models with the fastest and most scalable inference in the industry. We’ve been independently benchmarked as the leader in LLM inference speed and are driving cutting-edge innovation through projects like our own function calling and multimodal models. Fireworks is a Series C company valued at $4 billion and backed by top investors including Benchmark, Sequoia, Lightspeed, Index, and Evantic. We’re an ambitious, collaborative team of builders, founded by veterans of Meta PyTorch and Google Vertex AI.In the last few months alone we launched Fireworks Training, partnered with Microsoft Azure Foundry, and published research straight from our production systems. A few examples of what that looks like in practice: Frontier RL is cheaper than the mega-cluster narrative suggests: we ran cross-region rollouts using 98% sparse weight deltas and published what we learned. (blog) Open source agents with frontier advisors: matching frontier performance through training and harness engineering. (blog) The fine-tuning bottleneck is not the algorithm: integration friction and iteration speed are what actually stall teams; we documented the patterns across dozens of customer engagements. (blog) The Role: AI Field Engineers at Fireworks are the technical tip of the spear. You embed with our most ambitious customers and technology partners to turn complex AI problems into production systems, fast. The role sits at the intersection of engineering, product, and customer delivery. You are hands-on-keyboard building POCs, MVPs, and production integrations, while also holding your own in executive-level conversations about architecture, strategy, and business outcomes. You spend most of your time building. You ship code, run benchmarks, debug production issues, and architect deployments. But you also lead discovery conversations, align stakeholders, and translate customer pain points into product improvements that compress the feedback loop from field to roadmap. This is a role for engineers who are comfortable on-site with customers, building the relationships and trust that happen in person, not just over a call. The Segment As a Field Engineer in the AI Native segment you will work with the most innovative AI-native companies building at the frontier, where GenAI is the core product, not a feature, and where Fireworks is the platform they depend on to ship and scale it. These engagements move fast with fewer stakeholders, so you will spend more time in the code and iterate alongside their engineering teams, while still holding executive-level conversations on architecture and strategy. You will embed deeply with a small set of high-velocity accounts where the quality of your engineering is the relationship. What You'll Work On Technical Delivery and Deployment Build end-to-end POCs and MVPs alongside customer engineering teams, working inside their codebases, infrastructure, and constraints. For customers whose core product is built on GenAI, architect the inference foundations that capability depends on, and size deployments so they can scale in their market without infrastructure becoming the bottleneck. Run load tests and establish latency, throughput, and cost baselines against realistic customer traffic profiles, and tune deployments to hit those targets Deploy and validate new model families on inference frameworks (vLLM, SGLang), determining optimal shapes, quantization configs, and serving patterns across workloads. Model Strategy and Fine-Tuning Guide customers on model selection, fine-tuning strategy (SFT, DPO, RFT), and evaluation methodology. Build and run fine-tuning pipelines directly with customers, navigating trade-offs between model families, compute cost, and quality targets. Design and implement evaluation frameworks that measure production-quality metrics, not just benchmark scores. Customer Engagement and Stakeholder Management Many of our customers exist because of GenAI. Help them bake frontier model capabilities into their core offering and turn that into a durable competitive edge. Lead structured discovery conversations to unpack customer pain points, constraints, and success criteria before proposing solutions. Own the technical relationship from first engagement through production deployment. Embed with their engineering team as a peer, your credibility comes from what you build alongside them. Spend time on-site with customers. Build trust and momentum in person, embedding with their teams where the work happens. Product Feedback and Platform Improvement Identify recurring customer pain points and translate them into concrete product proposals, working directly with engineering and product to ship fixes and features. Codify repeatable deployment patterns and contribute them back to internal tooling, documentation, and the platform itself. Feed customer signals (deployment patterns, failure modes, feature gaps) back into the product roadmap with specificity and urgency. What We're Looking For: Minimum Qualifications 5+ years in a hands-on, customer-facing technical role: Forward Deployed Engineer, Applied AI Engineer, Solutions Architect, ML Engineer with field exposure, or technical founder. Demonstrated ability to build production software with customers, not just advise on it. You have shipped code running in someone else's production environment. Strong Python skills. Comfortable reading, writing, and debugging production code. Familiarity with Kubernetes and infrastructure engineering. Working knowledge of the LLM stack: inference trade-offs, model serving, fine-tuning workflows (SFT at minimum; DPO/RFT a strong plus). Experience with cloud infrastructure (AWS, Azure, GCP) and deploying models on GPU infrastructure. Exceptional communication: able to run a sharp discovery call, present to a VP, and debug a latency issue with an ML engineer in the same afternoon. Experience building or integrating agentic systems, tool-use chains, or AI-native developer toolchains. Preferred Qualifications 10+ years in technical field or engineering roles. Experience with inference serving frameworks (vLLM, SGLang, TensorRT-LLM) and tuning deployments for real workloads. Prior experience at a company with a forward-deployed or embedded engineering model (Palantir, Scale AI, Anthropic, OpenAI, BCG X, McKinsey Quantum Black, AI Native startups with FDE motions). Prior experience as a technical founder or early engineer at an AI-native company is a strong signal. Track record taking GenAI POCs from prototype to production-scale deployments. Experience with hyperscaler AI platforms (Azure AI Foundry, AWS Bedrock/SageMaker, GCP Vertex). Total compensation for this role also includes meaningful equity in a fast-growing startup, along with a competitive salary and comprehensive benefits package. Base salary is determined by a range of factors including individual qualifications, experience, skills, interview performance, market data, and work location. The listed salary range is intended as a guideline and may be adjusted.On Target Earnings (Plus Equity)$200,000—$260,000 USDWhy Fireworks AI? Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving. Build What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally. Ownership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI—no bureaucracy, just results. Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation. Fireworks AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.

false

About Us: At Fireworks, we’re building the future of generative AI infrastructure. Our platform delivers the highest-quality models with the fastest and most scalable inference in the industry. We’ve been independently benchmarked as the leader in LLM inference speed and are driving cutting-edge innovation through projects like our own function calling and multimodal models. Fireworks is a Series C company valued at $4 billion and backed by top investors including Benchmark, Sequoia, Lightspeed, Index, and Evantic. We’re an ambitious, collaborative team of builders, founded by veterans of Meta PyTorch and Google Vertex AI.In the last few months alone we launched Fireworks Training, partnered with Microsoft Azure Foundry, and published research straight from our production systems. A few examples of what that looks like in practice: Frontier RL is cheaper than the mega-cluster narrative suggests: we ran cross-region rollouts using 98% sparse weight deltas and published what we learned. (blog) Open source agents with frontier advisors: matching frontier performance through training and harness engineering. (blog) The fine-tuning bottleneck is not the algorithm: integration friction and iteration speed are what actually stall teams; we documented the patterns across dozens of customer engagements. (blog) The Role: AI Field Engineers at Fireworks are the technical tip of the spear. You embed with our most ambitious customers and technology partners to turn complex AI problems into production systems, fast. The role sits at the intersection of engineering, product, and customer delivery. You are hands-on-keyboard building POCs, MVPs, and production integrations, while also holding your own in executive-level conversations about architecture, strategy, and business outcomes. You spend most of your time building. You ship code, run benchmarks, debug production issues, and architect deployments. But you also lead discovery conversations, align stakeholders, and translate customer pain points into product improvements that compress the feedback loop from field to roadmap. This is a role for engineers who are comfortable on-site with customers, building the relationships and trust that happen in person, not just over a call. The Segment As a Field Engineer in the AI Native segment you will work with the most innovative AI-native companies building at the frontier, where GenAI is the core product, not a feature, and where Fireworks is the platform they depend on to ship and scale it. These engagements move fast with fewer stakeholders, so you will spend more time in the code and iterate alongside their engineering teams, while still holding executive-level conversations on architecture and strategy. You will embed deeply with a small set of high-velocity accounts where the quality of your engineering is the relationship. What You'll Work On Technical Delivery and Deployment Build end-to-end POCs and MVPs alongside customer engineering teams, working inside their codebases, infrastructure, and constraints. For customers whose core product is built on GenAI, architect the inference foundations that capability depends on, and size deployments so they can scale in their market without infrastructure becoming the bottleneck. Run load tests and establish latency, throughput, and cost baselines against realistic customer traffic profiles, and tune deployments to hit those targets Deploy and validate new model families on inference frameworks (vLLM, SGLang), determining optimal shapes, quantization configs, and serving patterns across workloads. Model Strategy and Fine-Tuning Guide customers on model selection, fine-tuning strategy (SFT, DPO, RFT), and evaluation methodology. Build and run fine-tuning pipelines directly with customers, navigating trade-offs between model families, compute cost, and quality targets. Design and implement evaluation frameworks that measure production-quality metrics, not just benchmark scores. Customer Engagement and Stakeholder Management Many of our customers exist because of GenAI. Help them bake frontier model capabilities into their core offering and turn that into a durable competitive edge. Lead structured discovery conversations to unpack customer pain points, constraints, and success criteria before proposing solutions. Own the technical relationship from first engagement through production deployment. Embed with their engineering team as a peer, your credibility comes from what you build alongside them. Spend time on-site with customers. Build trust and momentum in person, embedding with their teams where the work happens. Product Feedback and Platform Improvement Identify recurring customer pain points and translate them into concrete product proposals, working directly with engineering and product to ship fixes and features. Codify repeatable deployment patterns and contribute them back to internal tooling, documentation, and the platform itself. Feed customer signals (deployment patterns, failure modes, feature gaps) back into the product roadmap with specificity and urgency. What We're Looking For: Minimum Qualifications 5+ years in a hands-on, customer-facing technical role: Forward Deployed Engineer, Applied AI Engineer, Solutions Architect, ML Engineer with field exposure, or technical founder. Demonstrated ability to build production software with customers, not just advise on it. You have shipped code running in someone else's production environment. Strong Python skills. Comfortable reading, writing, and debugging production code. Familiarity with Kubernetes and infrastructure engineering. Working knowledge of the LLM stack: inference trade-offs, model serving, fine-tuning workflows (SFT at minimum; DPO/RFT a strong plus). Experience with cloud infrastructure (AWS, Azure, GCP) and deploying models on GPU infrastructure. Exceptional communication: able to run a sharp discovery call, present to a VP, and debug a latency issue with an ML engineer in the same afternoon. Experience building or integrating agentic systems, tool-use chains, or AI-native developer toolchains. Preferred Qualifications 10+ years in technical field or engineering roles. Experience with inference serving frameworks (vLLM, SGLang, TensorRT-LLM) and tuning deployments for real workloads. Prior experience at a company with a forward-deployed or embedded engineering model (Palantir, Scale AI, Anthropic, OpenAI, BCG X, McKinsey Quantum Black, AI Native startups with FDE motions). Prior experience as a technical founder or early engineer at an AI-native company is a strong signal. Track record taking GenAI POCs from prototype to production-scale deployments. Experience with hyperscaler AI platforms (Azure AI Foundry, AWS Bedrock/SageMaker, GCP Vertex). Total compensation for this role also includes meaningful equity in a fast-growing startup, along with a competitive salary and comprehensive benefits package. Base salary is determined by a range of factors including individual qualifications, experience, skills, interview performance, market data, and work location. The listed salary range is intended as a guideline and may be adjusted.On Target Earnings (Plus Equity)$200,000—$260,000 USDWhy Fireworks AI? Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving. Build What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally. Ownership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI—no bureaucracy, just results. Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation. Fireworks AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.

false

About the TeamThe GTM Data Science team partners with Go-to-Market, Technical Success, Product, Engineering, RevOps, and Strategic Finance to build the shared intelligence layer for OpenAI's B2B business. The team turns product usage, customer behavior, revenue, field activity, and customer feedback into rigorous insight products that help leaders and field teams understand where customers are succeeding, where adoption is blocked, and what actions will accelerate durable growth.We are building systems that make customer intelligence proactive: surfacing risk, expansion potential, product gaps, and repeatable playbooks before they show up as escalations or missed opportunities.About the RoleAs the Applied Data Science & Insights Lead for GTM Intelligence Solutions and Technical Success, you will be a hands-on technical leader responsible for shaping how OpenAI measures, understands, and improves customer adoption across our B2B products. You will build AI/ML-powered intelligence products that connect account health, product usage, customer lifecycle, support tier, qualitative sentiment, commercial context, and field actions into a practical operating system for GTM and Technical Success.This role will build the data science foundation for Technical Success: defining the metrics, models, operating insights, and decision systems that help the team scale customer adoption and expansion with rigor.You will also be expected to build and lead a small mighty team over time: setting direction, hiring and developing talent, creating operating cadences, and holding a high bar for technical rigor and business impact.You will lead the development of models, metrics, and decision systems that recommend what GTM and Technical Success teams should do next, explain why, and measure whether those interventions worked. Your work will help customers move from pilots to production, deepen usage across products, identify high-value use cases, reduce churn risk, and create a faster feedback loop from the field back to Product and Research.This role is based in San Francisco, CA. We use a hybrid work model of three days in the office per week and offer relocation assistance to new employees. The VisionBuild a unified GTM intelligence layer that connects product telemetry, customer health, revenue, support tier, lifecycle stage, field activity, and qualitative feedback.Turn adoption breadth, usage depth, sentiment, and customer maturity signals into next-best-action systems for Technical Success and field teams.Create a measurement foundation for Technical Success playbooks, including whether recommended actions were taken and whether they improved customer outcomes.Help OpenAI understand customer happy paths: the use cases, product behaviors, and interventions that lead to durable adoption, expansion, and retention.Productize insights into workflows used by Technical Success, Sales, RevOps, Finance, Product, and executive leadership.In This Role, You Will:Define and lead the roadmap for GTM Intelligence and Technical Success insight products in partnership with cross-functional leaders.Build the data science foundation for Technical Success, including core metrics, customer health definitions, intervention measurement, and reusable playbook analytics.Develop propensity score models for model and product feature adoption, helping Technical Success and GTM identify which customers are most likely to adopt, which interventions can move adoption, and where support should focus.Build, mentor, and lead a small team of data scientists and cross-functional analytics partners as the GTM Intelligence function scales.Set technical standards for modeling, metrics, experimentation, documentation, and production readiness across the team's work.Create team operating rhythms that balance urgent field needs with durable roadmap execution, quality review, and stakeholder alignment.Build predictive and causal models for customer health, expansion propensity, churn risk, adoption depth, use-case fit, and intervention effectiveness.Design next-best-action systems that identify account opportunities and risks, recommend playbooks, and explain the evidence behind each recommendation.Partner with Technical Success leaders to enumerate playbooks and actions, instrument action tracking, and measure outcomes over time.Develop customer segmentation and benchmarking frameworks across products, industries, personas, support tiers, and lifecycle stages.Create scalable insight products that are embedded into field workflows rather than living only as one-off analyses or static dashboards.Translate field feedback and account-level patterns into clear product and GTM recommendations for senior leadership.Collaborate with Data Engineering and RevOps to improve the data foundations connecting product telemetry, Salesforce, support signals, revenue, and qualitative feedback.Maintain a high bar for analytical rigor, including causal evaluation, validation, data quality, and clear caveats.You Might Thrive in This Role If You Have:10+ years of experience in applied data science, analytics, machine learning, quantitative strategy, or a closely related field.Deep technical skill in SQL and Python, with the ability to move from raw tables to production-quality models, metrics, and decision systems.Strong applied experience with statistical modeling, causal inference, machine learning, customer segmentation, churn or health modeling, or recommendation systems.Experience with propensity score modeling, uplift modeling, or related causal methods for adoption, activation, retention, or product feature usage.Experience building production or workflow-embedded data products for GTM, sales, customer success, technical success, growth, or enterprise SaaS teams.Product intuition and business judgment for turning ambiguous questions into repeatable models, tools, metrics, and operating cadences.Excellent communication skills, including the ability to distill complex analysis into clear recommendations for technical partners, field teams, and executives.Comfort partnering across technical and non-technical teams, including Product, Engineering, Technical Success, Sales, RevOps, Finance, and Data Engineering.A track record of operating autonomously in fast-moving environments and raising the quality of how teams use data to make decisions.Experience leading teams or serving as a technical lead for multi-person data science initiatives, including mentoring, roadmap-setting, and quality review.Ability to hire, develop, and retain strong data science talent while creating a collaborative, high-accountability team culture.An advanced degree in a quantitative field, or equivalent practical experience.About OpenAIOpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement.Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form. No response will be provided to inquiries unrelated to job posting compliance.We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link.OpenAI Global Applicant Privacy PolicyAt OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.

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About the Team OpenAI’s API Multicloud team is responsible for extending OpenAI’s API platform into strategic cloud environments, starting with AWS. The team’s mission is to distribute OpenAI’s API broadly and safely by enabling key API technologies in cloud-native environments, in close partnership with Amazon and internal teams across Codex, Research, Safety Systems, and Applied.The team is focused on bringing core developer and enterprise capabilities into cloud-native environments, including cloud-hosted Codex, model customization / post-training as a service, and new stateful runtime environments for agentic workloads. This work sits at the intersection of production ML systems, developer platforms, model behavior, and large-scale infrastructure.About the RoleWe’re looking for a backend engineer who can quickly understand OpenAI’s models, products, and systems, then adapt first-party deployments for other cloud platforms. You’ll build backend services, APIs, SDK integrations, authentication flows, and cloud service infrastructure that let developers use OpenAI capabilities in the cloud environments where they already build. This role involves working across teams, sometimes embedded with partner product groups, to ship products quickly and across multiple platforms at the same time. It’s a strong fit for engineers who have built developer tools, especially AI-powered tools, communicate clearly across technical boundaries, and can shape architectures that support different deployment models; experience building cloud services is a strong plus.In this role, you will:Build backend and infrastructure systems that extend OpenAI’s API platform into cloud-native environments, like AWS.Design and ship cloud-contained products that allow customers to use OpenAI capabilities while keeping workloads and data within cloud environments.Help stand up cloud-hosted Codex experiences powered by the OpenAI Responses API.Build the infrastructure and runtime abstractions for a stateful, cloud-optimized agentic platform.Partner closely with external cloud partners as well as internal teams across Codex, Research, and Safety Systems to translate emerging capabilities into production-ready systems.Improve the reliability, scalability, observability, and operational maturity of the services underpinning these products.Help shape the technical direction of a new and growing team as it scales from an early core group into a larger engineering organization.Your background might look something like:7+ years of professional engineering experience (excluding internships) in relevant roles at tech and product-driven companies.Proficient in one or more backend languages (e.g., Python, Go, Rust, Typescript or similar) and distributed systems concepts.Experience building developer tools, especially AI-powered tools, excellent technical communication, and the ability to shape architectures that support different deployment models. Experience building cloud services is a strong plus.Experience turning ambiguous, fast-moving technical problems into pragmatic, durable systems.Experience working with cloud infrastructure primitives and production service ownership.Experience collaborating cross-functionally with platform, research, safety, or external partner teams is a plus.Interest in AI infrastructure, agentic systems, and developer platforms.Interest in AI/ML (direct experience not required)Proven ability to thrive in fast-growing, product-driven companies by effectively navigating loosely defined tasks and managing competing priorities or deadlines.About OpenAIOpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement.Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form. No response will be provided to inquiries unrelated to job posting compliance.We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link.OpenAI Global Applicant Privacy PolicyAt OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.

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About the teamThe Applied team safely brings OpenAI's technology to the world. Our team launched ChatGPT, Advanced Voice Mode, Deep Research, and many other products, supporting scalable infrastructure and driving safe, responsible deployment. Our customers rely on our APIs to build transformative products, while we ensure responsible usage and platform integrity.The Integrity team, within Applied Engineering, safeguards our platform by proactively identifying misuse, preventing abuse, and protecting users. We're seeking an experienced, hands-on Tech Lead Manager (Web Safety) to shape the future direction, architect advanced system protections, and lead a focused team of Software Engineers.In this role, you will:Architect and build next-generation system protections, directly contributing through hands-on design, model training, and deployment strategies.Lead and manage a small, senior team of Engineers, empowering them with clear direction and autonomy.Collaborate closely with Research, Safety, Product, and Policy teams to leverage existing tools and drive cutting-edge advancements.Utilize state-of-the-art models to detect and prevent problematic content effectively.Establish robust evaluation frameworks and metrics, clearly measuring progress and identifying areas for improvement.Support your team's professional growth and maintain high performance through mentorship and clear career progression.You might thrive in this role if you:Bring extensive hands-on experience managing engineering teams, ideally in web safety, content integrity, or related domains.Enjoy being deeply involved technically—actively designing solutions, training models, and guiding deployment processes alongside your team.Excel at collaborating across teams to integrate existing tools and thoughtfully architect new solutions.Possess strong emotional intelligence, empathy, and the ability to effectively connect with colleagues.Embrace ambiguity and rapidly changing circumstances as opportunities to create clarity, structure, and impactful solutions.About OpenAIOpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement.Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form. No response will be provided to inquiries unrelated to job posting compliance.We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link.OpenAI Global Applicant Privacy PolicyAt OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.

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About the TeamThe Technical Success team is responsible for ensuring the safe and effective deployment of ChatGPT and OpenAI API applications for developers and enterprises. We act as a trusted advisor and thought partner for our customers, ensuring developers and enterprises maximize value from our models and products. As a Solutions Engineer, you’ll help companies across industries transform their business through solutions such as customer service, automated content generation, and novel applications that make use of our newest, most exciting models.About the RoleWe are looking for a solutions-oriented technical leader to engage with customers post-sale and ensure they realise tangible business value from their investment in OpenAI's technologies. You will work closely with senior leaders and technical teams within customer organizations to establish GenAI roadmaps, strategies, prioritize high-value use cases, and guide projects from early prototyping through enterprise-grade production deployments.You will take a holistic view of each customer’s architecture and operations, designing solutions that leverage ChatGPT, OpenAI APIs, and our broader ecosystem of tools and services. You will work cross-functionally with Sales, Solutions Engineering, Applied Research, and Product teams, and report to the Head of Solutions Architecture for your segment.This role is based in MadridIn this role, you will:Serve as the primary technical subject matter expert post-sale for a portfolio of customers, embedding deeply with them to design and deploy GenAI solutions.Engage with senior business and technical stakeholders to identify, prioritize, and validate the highest-value GenAI applications in their roadmap.Accelerate customer time to value by providing architectural guidance, building hands-on prototypes, and advising on best practices for scaling solutions in production.Maintain strong relationships with leadership and technical teams to drive adoption, expansion, and successful outcomes.Contribute to open-source resources and enterprise-facing technical documentation to scale best practices across customers.Share learnings and collaborate with internal teams to inform product development and improve customer outcomes.Codify knowledge and operationalize technical success practices to help the Solutions Architecture team scale impact across industries and customer types.You might thrive in this role if you:Have 5+ years of technical consulting, post-sales engineering, solutions architecture, or similar experience working directly with customers.Are a strong communicator, able to explain technical and business concepts clearly to executive and practitioner audiences alike.Have experience leading complex deployments of Generative AI or traditional machine learning systems, ideally including infrastructure and network architecture considerations.Possess hands-on proficiency in languages like Python, JavaScript, or similar, and are comfortable building prototypes or proofs of concept.Take end-to-end ownership of challenges, proactively acquiring new skills or knowledge as needed to drive success.Bring a humble, collaborative mindset and an eagerness to support teammates and customers alike.Thrive in fast-paced environments, adeptly managing multiple workstreams and prioritizing for the highest customer impact.About OpenAIOpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement.Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form. No response will be provided to inquiries unrelated to job posting compliance.We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link.OpenAI Global Applicant Privacy PolicyAt OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.

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Meet SienaSiena is the Agent of Record for consumer brands. One agent that owns the entire customer journey, running on a shared intelligence layer no point tool can replicate.Consumer brands run ten or more disconnected tools to manage their customers. We collapse that stack into a single agent. It started with support, the hardest surface a brand has, and it expands from there: Shopping Agent, Social Agent, Siena working alongside a brand's own operators. One agent across every surface, with shared memory, shared context, and shared tools.Hundreds of brands already run real customer volume on Siena in production. This is built, working, and growing, and we are racing to build what comes next.The teamWe are a small, fully distributed, global team. We have built leverage into how we work, which is what lets a team this size move at the speed we do.We hire athletes. Everyone here owns real outcomes, defaults to writing over meetings, communicates with directness, and acts the moment they see something worth acting on. Low ego, high standards, fast clock.If you want to own real problems rather than close tickets, keep reading.About the RoleAs a Senior Full Stack Engineer, you will own meaningful parts of the platform end to end. You will take ambiguous problems, work out the right approach, and ship reliable, high-performance systems that real brands depend on every day.You will work closely with product to shape scope and roadmap, make real architectural decisions within your area, and do your best work without being managed task by task. This role is for engineers who want ownership and can carry it. What You'll DoOwn features and systems across the full stack, frontend, backend, and infrastructure, from problem definition through production.Partner with product to break down ambiguous scope and ship in iterative, high-impact releases.Make real architectural and design decisions in your area, and explain the reasoning so the team can build on it.Take cutting-edge language models into enterprise customer workflows, where reliability and safety actually matter.Solve hard engineering problems: API performance, microservices, scaling across channels and brands.Maintain and improve our AWS infrastructure with a DevOps mindset.Raise the bar around you. Share what you know, review thoughtfully, and help teammates level up.Who You Are5+ years of software engineering, with real ownership of systems you have shipped and run in production.Strong full-stack engineer in our core stack: Node.js, React, TypeScript, Express.Solid with AWS (Fargate, Lambda, EC2, SQS) and with PostgreSQL and Redis.Comfortable with CI/CD pipelines (CodePipeline, GitHub Actions, or similar).Experienced with microservices and scalable systems.Product-focused. You think past the code to customer outcomes and business impact.You have shipped LLM-powered features to production. This is a must, not a bonus. You know what breaks when models meet real users.High-ownership and direct. You take initiative, fix problems without waiting to be told, and say openly when something is not working.Bonus PointsNext.js.Infrastructure as Code (CDK, Terraform).Python.Event-driven architecture.Background in customer support or CX software.Why Siena?Real ownership. You will own problems, not tasks, with the autonomy to solve them your way and the trust to do it without a manager in your inbox.Work that ships and matters. What you build reaches real consumer brands fast.A category we are creating. The Agent of Record is new ground, with unsolved problems and cutting-edge models, not a crowded field to compete in.A team that respects your judgment. Globally distributed, async-first, low bureaucracy. We hire adults and treat you like one.Great salary plus equity or stock grants. Own a piece of what you build.Learning budget. If you are growing, so are we.AI-fluency by default. Few places will push your work with production AI further or faster.Our valuesThe people who thrive here are curious, customer-obsessed, and take ownership without being asked. They fix problems first and explain later. They're direct about feedback—both giving and receiving it—because they care more about getting things right than being polite.They maintain high standards while moving at startup speed, and they build real relationships with teammates because they know that's how great work gets done. When things get tough, they adapt and keep pushing forward. Our approach to AIThe people who thrive here treat AI like a natural extension of themselves. They've built their own ecosystem of agents - some for research, others for debugging, writing, analysis, or writing code. They know which AI works best for what problem.Everyone gets premium accounts (ChatGPT, Claude, Perplexity Pro, Cursor, Lovable) plus a quarterly budget for new tools. But the magic happens in how we share knowledge. Demo days where someone shows off a clever workflows. Slack threads about which model handles a specific use case better. Learning from each other's AI workflows.At Siena, we’re not just looking for people who can do a job. We’re looking for people who want to break boundaries, create the future, and reshape industries. If that’s you, we look forward to your application.Interview processSilver Screening InterviewSilver Technical InterviewClient Screening InterviewClient Takehome + DefenseClient Behavioral Interview

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  About the Company Armada is the hyperscaler for the edge, delivering modular AI infrastructure from first deployment to AI factory with speed, scale and sovereignty. Named one of Fast Company's Most Innovative Companies and to the CNBC Disruptor 50, Armada’s solutions are deployed in over 60 countries globally for organizations ranging from energy to defense.  With nearly half a billion dollars in funding, Armada is backed by top investors such as Microsoft (M12), Founders Fund, and BlackRock, and has collaborations and partnerships including NVIDIA, Palantir and Dell Technologies. We are looking for the most brilliant minds in the world to join us.  Working at Armada means taking ownership, driving autonomy, and delivering impact. You’ll tackle challenges that haven’t been solved before and help build something transformative from the ground up. What you do here will not only define your career but help further Armada’s mission to bridge the digital divide for customers around the world.      About the role At Armada, we are unlocking the limitless potential of AI to transform operations and improve lives in some of the most remote locations on Earth. From the expansive mines of Australia to the oil fields of Northern Canada, and the coffee plantations of Colombia, Armada offers a unique opportunity to tackle exciting AI and ML challenges on a global scale. We are actively seeking passionate AI Engineers with hands-on expertise across a range of domains, including real-time computer vision, statistical machine learning, natural language processing, transformers, control and navigation, reinforcement learning, and large-scale distributed AI systems. Ideal candidates will possess strong skills in machine learning (ML), deep learning (DL), and real-time computer vision techniques. You will be responsible for building ML/DL models tailored to specific challenges, preparing datasets for testing, evaluating model performance, and deploying solutions in production environments. Familiarity with containerization, microservices architecture, and the ability to independently deploy ML models into production is essential. If you are a self-driven individual with a passion for cutting-edge AI, we want to hear from you. Armada offers an unparalleled opportunity to confront some of the most thrilling AI and ML challenges in the world. Join our dynamic AI Engineering team as we deliver disruptive edge-compute systems capable of autonomous learning, prediction, and adaptation using vast, real-time datasets. We are pioneers in developing high-performance computing solutions for self-driving cars, camera networks, robotics, drones, conversational agents, and real-time monitoring and diagnostic systems. Our vision is to empower AI systems to seamlessly and securely interact with the complexities and uncertainties of the real world, and our mission is to bridge the digital divide in the process.  Location. This role is office-based at our Bellevue, Washington office.  What You'll Do (Key Responsibilities) Translating business requirements into requirements for AI/ML models. Preparing data to train and evaluate AI/ML/DL models. Building AI/ML/DL models by applying state-of-the-art algorithms, especially transformers. In some cases, leverage existing algorithms from academic or industrial research. Testing, evaluating the AI/ML/DL models, benchmarking their quality, and publishing the models, data sets, and evaluations. Deploying the models in production by containerizing the models. Working with customers and internal employees to refine the quality of the models. Establishing continuous learning pipelines for models with online learning or transfer learning. Building and deploying containerized applications on the cloud or on-premise environments Required Qualifications BS or MS degree in computer science, computational. science/engineering, or related technical field (or equivalent experience). 3+ years of work-related experience in software development with good Python, Java, and/or C/C++ programming skills. Familiarity with containers, numeric libraries, modular software design. Hands-on expertise with traditional statistical machine learning techniques as well as deep-learning and natural language processing modeling. Expertise in supervised, unsupervised, and transfer learning techniques. Hands-on expertise in machine learning techniques and algorithms with a strong background in state-of-the-art DNN architectures (Transformers, CNN, R-CNN, RNN, BERT, GAN, autoencoders, etc.) and experience in developing or using major deep learning frameworks (e.g., PyTorch, Tensorflow, etc). Experience with solving and using machine learning for real-world problems. Preferred Experience and Skills Demonstrable experience in building, programming, and integrating software and hardware for autonomous or robotic systems. Proven experience producing computationally efficient software to meet real-time requirements. Background with container platforms such as Kubernetes. Strong analytical skills with a bias for action. Strong time-management and organization skills to thrive in a fast-paced, dynamic environment. Solid written and oral communications skills. Good teamwork and interpersonal skills. Compensation For U.S. Based candidates: To ensure fairness and transparency, the starting base salary range for this role for candidates in the U.S. are listed below, varying based on location experience, skills, and qualifications. In addition to base salary, this role will also be offered equity and subsidized benefits (details available upon request). Benefits Competitive base salary and equity Medical, dental, and vision (subsidized cost) Health savings accounts (HSA), flexible spending accounts (FSA), and dependent care FSAs (DCFSA) Retirement plan options, including 401(k) and Roth 401(k) Unlimited paid time off (PTO) 14 paid company holidays per year #LI-SM2 #LI-Onsite   Compensation$154,560—$193,200 USD  You're a Great Fit if You're A go-getter with a growth mindset. You're intellectually curious, have strong business acumen, and actively seek opportunities to build relevant skills and knowledge  A detail-oriented problem-solver. You can independently gather information, solve problems efficiently, and deliver results with a "get-it-done" attitude  Thrive in a fast-paced environment. You're energized by an entrepreneurial spirit, capable of working quickly, and excited to contribute to a growing company A collaborative team player. You focus on business success and are motivated by team accomplishment vs personal agenda  Highly organized and results-driven. Strong prioritization skills and a dedicated work ethic are essential for you    Equal Opportunity Statement At Armada, we are committed to fostering a work environment where everyone is given equal opportunities to thrive. As an equal opportunity employer, we strictly prohibit discrimination or harassment based on race, color, gender, religion, sexual orientation, national origin, disability, genetic information, pregnancy, or any other characteristic protected by law. This policy applies to all employment decisions, including hiring, promotions, and compensation. Our hiring is guided by qualifications, merit, and the business needs at the time.   Unsolicited Resumes and Candidates Armada does not accept unsolicited resumes or candidate submissions from external agencies or recruiters. All candidates must apply directly through our careers page. Any resumes submitted by agencies without a prior signed agreement will be considered unsolicited and Armada will not be obligated to pay any fees.  

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  About the Company Armada is the hyperscaler for the edge, delivering modular AI infrastructure from first deployment to AI factory with speed, scale and sovereignty. Named one of Fast Company's Most Innovative Companies and to the CNBC Disruptor 50, Armada’s solutions are deployed in over 60 countries globally for organizations ranging from energy to defense.  With nearly half a billion dollars in funding, Armada is backed by top investors such as Microsoft (M12), Founders Fund, and BlackRock, and has collaborations and partnerships including NVIDIA, Palantir and Dell Technologies. We are looking for the most brilliant minds in the world to join us.  Working at Armada means taking ownership, driving autonomy, and delivering impact. You’ll tackle challenges that haven’t been solved before and help build something transformative from the ground up. What you do here will not only define your career but help further Armada’s mission to bridge the digital divide for customers around the world.      About the role At Armada, we are unlocking the limitless potential of AI to transform operations and improve lives in some of the most remote locations on Earth. From the expansive mines of Australia to the oil fields of Northern Canada, and the coffee plantations of Colombia, Armada offers a unique opportunity to tackle exciting AI and ML challenges on a global scale. We are actively seeking passionate AI Engineers with hands-on expertise across a range of domains, including real-time computer vision, statistical machine learning, natural language processing, transformers, control and navigation, reinforcement learning, and large-scale distributed AI systems. Ideal candidates will possess strong skills in machine learning (ML), deep learning (DL), and real-time computer vision techniques. You will be responsible for building ML/DL models tailored to specific challenges, preparing datasets for testing, evaluating model performance, and deploying solutions in production environments. Familiarity with containerization, microservices architecture, and the ability to independently deploy ML models into production is essential. If you are a self-driven individual with a passion for cutting-edge AI, we want to hear from you. Armada offers an unparalleled opportunity to confront some of the most thrilling AI and ML challenges in the world. Join our dynamic AI Engineering team as we deliver disruptive edge-compute systems capable of autonomous learning, prediction, and adaptation using vast, real-time datasets. We are pioneers in developing high-performance computing solutions for self-driving cars, camera networks, robotics, drones, conversational agents, and real-time monitoring and diagnostic systems. Our vision is to empower AI systems to seamlessly and securely interact with the complexities and uncertainties of the real world, and our mission is to bridge the digital divide in the process.  Location. This role is office-based at our Bellevue, Washington office.  What You'll Do (Key Responsibilities) Translating business requirements into requirements for AI/ML models. Preparing data to train and evaluate AI/ML/DL models. Building AI/ML/DL models by applying state-of-the-art algorithms, especially transformers. In some cases, leverage existing algorithms from academic or industrial research. Testing, evaluating the AI/ML/DL models, benchmarking their quality, and publishing the models, data sets, and evaluations. Deploying the models in production by containerizing the models. Working with customers and internal employees to refine the quality of the models. Establishing continuous learning pipelines for models with online learning or transfer learning. Building and deploying containerized applications on the cloud or on-premise environments Required Qualifications BS or MS degree in computer science, computational. science/engineering, or related technical field (or equivalent experience). 3+ years of work-related experience in software development with good Python, Java, and/or C/C++ programming skills. Familiarity with containers, numeric libraries, modular software design. Hands-on expertise with traditional statistical machine learning techniques as well as deep-learning and natural language processing modeling. Expertise in supervised, unsupervised, and transfer learning techniques. Hands-on expertise in machine learning techniques and algorithms with a strong background in state-of-the-art DNN architectures (Transformers, CNN, R-CNN, RNN, BERT, GAN, autoencoders, etc.) and experience in developing or using major deep learning frameworks (e.g., PyTorch, Tensorflow, etc). Experience with solving and using machine learning for real-world problems. Preferred Experience and Skills Demonstrable experience in building, programming, and integrating software and hardware for autonomous or robotic systems. Proven experience producing computationally efficient software to meet real-time requirements. Background with container platforms such as Kubernetes. Strong analytical skills with a bias for action. Strong time-management and organization skills to thrive in a fast-paced, dynamic environment. Solid written and oral communications skills. Good teamwork and interpersonal skills. Compensation For U.S. Based candidates: To ensure fairness and transparency, the starting base salary range for this role for candidates in the U.S. are listed below, varying based on location experience, skills, and qualifications. In addition to base salary, this role will also be offered equity and subsidized benefits (details available upon request). Benefits Competitive base salary and equity Medical, dental, and vision (subsidized cost) Health savings accounts (HSA), flexible spending accounts (FSA), and dependent care FSAs (DCFSA) Retirement plan options, including 401(k) and Roth 401(k) Unlimited paid time off (PTO) 14 paid company holidays per year #LI-SM2 #LI-Onsite   Compensation$154,560—$193,200 USD  You're a Great Fit if You're A go-getter with a growth mindset. You're intellectually curious, have strong business acumen, and actively seek opportunities to build relevant skills and knowledge  A detail-oriented problem-solver. You can independently gather information, solve problems efficiently, and deliver results with a "get-it-done" attitude  Thrive in a fast-paced environment. You're energized by an entrepreneurial spirit, capable of working quickly, and excited to contribute to a growing company A collaborative team player. You focus on business success and are motivated by team accomplishment vs personal agenda  Highly organized and results-driven. Strong prioritization skills and a dedicated work ethic are essential for you    Equal Opportunity Statement At Armada, we are committed to fostering a work environment where everyone is given equal opportunities to thrive. As an equal opportunity employer, we strictly prohibit discrimination or harassment based on race, color, gender, religion, sexual orientation, national origin, disability, genetic information, pregnancy, or any other characteristic protected by law. This policy applies to all employment decisions, including hiring, promotions, and compensation. Our hiring is guided by qualifications, merit, and the business needs at the time.   Unsolicited Resumes and Candidates Armada does not accept unsolicited resumes or candidate submissions from external agencies or recruiters. All candidates must apply directly through our careers page. Any resumes submitted by agencies without a prior signed agreement will be considered unsolicited and Armada will not be obligated to pay any fees.  

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What You’ll DoTranslate cutting-edge research into production-ready machine learning systemsDesign, build, and deploy end-to-end ML models and pipelinesDevelop and optimize models for image and video processingOwn the full ML lifecycle: experimentation, training/fine-tuning, evaluation, and deploymentRapidly prototype using open-source models and adapt them for product needsConduct experiments, analyze results, and iterate to improve performanceCollaborate with researchers and cross-functional teams (product, engineering, design) to deliver ML solutions at scaleParticipate with advancements in machine learning and apply them to continuously improve products What We’re Looking For Required QualificationsMS/PhD in Computer Science, Electrical Engineering, or related fieldStrong research experience with familiarity in top conferences (e.g., CVPR, ICCV, NeurIPS)5+ years of experience in Python and proficiency in Java, C++, or ScalaStrong understanding of diffusion modelsStrong understanding of multi-threading and memory managementSolid knowledge of ML architectures: CNNs and TransformersExperience with PyTorch or TensorFlowExperience building end-to-end ML deployment and inference systems, especially for low-latency, real-time applicationsExperience deploying ML models in cloud environments (AWS preferred)Experience with experiment tracking systems and ML workflows Nice to HaveExperience in low level optimisation, cuda etc.Experience productionizing and scaling ML models in real-world systemsContributions to open-source projectsExperience with MLOps tools or distributed training systemsFamiliarity with relational databases (Postgres/MySQL)Experience handling large-scale data using tools like SparkReka's MissionReka's mission is to build useful multimodal artificial intelligence and use it to empower organisations and businesses. We are a globally distributed foundation model startup, headquartered in the San Francisco Bay Area, California. Embracing a remote-first approach, our team brings together top talent from around the world. Our founding team, along with many of our team members, has contributed to many of the breakthroughs in AI over the past decade. Why Reka?An Elite Team: Collaborate with top-tier engineers, researchers, operators from renowned organizations like Google DeepMind and Facebook AI Research (FAIR) and successful startups, driving innovation in cutting-edge AI technology.Massive Market Opportunity: Be part of a rapidly growing industry poised to transform multiple sectors globally, offering the chance to make a significant impact.Mission-Driven Environment: Work alongside a collaborative, mission-focused team dedicated to advancing AI for meaningful applications.Inclusive and Open Culture: Thrive in an open and inclusive work environment that values diverse perspectives and fosters creativity.Generous Benefits: Enjoy 5 weeks of paid leave to recharge, comprehensive healthcare benefits including vision and dental, and additional perks that support your well-being.Visa Support: We provide visa assistance, including H1B and OPT transfers, for US employees to ensure a smooth transition and support your career with us.

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Maincode is an Australian AI research company building Matilda, an assistant that understands complex work, reasons over context, and takes meaningful action safely.The AI Research Residency is a paid 3 to 6 month program for late-stage PhD students and exceptional early-career researchers who want to pursue high-impact AI research grounded in real systems.Residents work closely with Maincode's research and engineering teams, with dedicated access to large-scale GPU compute and our in-house research infrastructure. You will explore open problems, run experiments at scale, and produce work that can contribute to top-tier publications, open research, infrastructure, or the systems behind Matilda.This role is research-first, but applied. Strong projects may take the form of model research, evaluations, infrastructure, technical engineering work, or product-facing research that improves Matilda and future Maincode systems.Research areasWe are interested in research that makes AI systems more capable, reliable, efficient, and useful in the real world.The residency program is primarily focused on the following areas:AgentsTool use, planning, memory, computer control, multi-agent systems, and safe execution in real-world environments.Long-context reasoning, workflow understanding, state tracking, memory systems, and methods for maintaining coherence across complex tasks.Safety and evaluationCapability evaluations, alignment, oversight, interpretability, robustness, red-teaming, and benchmarks for real-world task completion.Training and algorithmsLanguage model training, reinforcement learning, reasoning methods, optimisation, architectures, and new approaches to improving model behaviour.DataData curation, filtering, synthetic data, mixture design, quality verification, pruning, and principled approaches to training signal.Multimodal systemsVision-language models, grounding, perception, multimodal reasoning, and systems that combine language, visual context, and action.Efficiency and infrastructureTraining and inference efficiency, kernels, parallelism, sharding, decoding, quantisation, scheduling, and systems for running large models reliably.ResponsibilitiesLead research that advances Maincode's work on capable, useful, and trustworthy AI systems.Design and execute experiments, develop new research directions, and collaborate closely with our researchers and engineers.Produce research outputs suitable for top-tier conferences, journals, technical reports, open-source releases, or deployment in Matilda and future Maincode systems. QualificationsLate-stage PhD student, recent PhD graduate, or exceptional early-career researcher.Strong research taste and the ability to identify important problems before they are obvious.Experience publishing, preprinting, or producing high-quality research in AI, machine learning, or adjacent technical fields.Ability to define and execute independent research under uncertainty.Interest in building AI systems that can reason, act, and operate reliably in complex real-world environments.What you will have access toDedicated access to Maincode's GPU compute and research infrastructure.Close collaboration with a small, high-calibre team across AI research, systems engineering, product engineering, and design.Support for top-tier conference and journal submissions, with the opportunity for strong research to contribute to Matilda and future Maincode systems.A research environment focused on deep work, technical seriousness, and real-world impact.

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Who we are:    AI is the new electricity: Just as electricity transformed numerous industries starting 100 years ago, AI is now poised to do the same. AI Fund is a venture studio founded by Dr. Andrew Ng in 2017. Our portfolio companies use  AI technology to build  applications across numerous industry sectors. The AI Fund team combines their experiences as AI pioneers, entrepreneurs, venture capitalists, investors, and operators. We are backed by a $390-million dollar fund from top-tier global corporations and VC firms. Our purpose is to build AI companies that move humanity forward   What We’re Looking For:   For the Engineer in Residence role, we are seeking an AI Full-Stack Engineer with fluency in software development and proficient in rapid prototyping. You should possess intense curiosity and leverage modern Generative AI frameworks. The ideal candidate thrives in fast-paced environments where the goal is to quickly build and iterate on full-stack applications leveraging cutting-edge AI techniques, as well as guiding technical vision and defining architectural patterns.   You're passionate about staying ahead of the curve in AI constantly exploring new tools, frameworks, and best practices. You’ll collaborate closely with Andrew Ng to bring experimental ideas to life through functional prototypes.   This role puts you in the front row of where AI is heading, working with technologies that are helping shape the future. If you’re driven by ambition, innovation, and the thrill of building what’s next this is the role for you. What you will do: Drive rapid engineering efforts to build full-stack applications using GenAI tools, enabling deep exploration of product ideas, user experiences, and technical feasibility Apply GenAI building blocks (prompt engineering, graphDBs, vectorDBs, agentic frameworks, evals, and guardrails) to real-world development Make key decisions around infrastructure and platform choices, balancing short-term prototyping needs with long-term scalability Collaborate cross-functionally with product, design, and AI experts to create, test, and iterate on new concepts Use AI-assisted coding tools (e.g., Cursor, Windsurf, GitHub Copilot, Claude Code) to enhance productivity and speed of iteration Gather user feedback and iterate quickly based on insights to improve usability and effectiveness Deploy and manage applications on cloud infrastructure (e.g., AWS, GCP, Supabase) Build and integrate APIs and third-party services Participate in architecture discussions and technical planning Identify and troubleshoot issues across the stack Contribute to improving development processes, tools, and team practices Stay current with industry trends and emerging technologies What you must bring: Demonstrated experience building applications that incorporate Generative AI Proficiency with GenAI tools and frameworks: prompt engineering, Retrieval-Augmented Generation (RAG), vector databases (e.g., Pinecone, Weaviate), agentic platforms (e.g., LangGraph, CrewAI), LLM evals, and guardrails Experience with front-end technologies like JavaScript, HTML, CSS, and modern frameworks Strong back-end skills, particularly with Python Familiarity with SQL and NoSQL databases (PostgreSQL, MongoDB, etc.) Hands-on experience using AI-assisted coding tools (e.g., Cursor, Claude Code) and understanding of best practices for effective use Proven track record of architecting, implementing, and deploying scalable and maintainable AI-powered systems Expertise in designing end-to-end system architectures involving front-end, back-end, APIs, and AI/ML components Deep curiosity and a fast-learning mindset especially around emerging trends in AI Ability to write scrappy, disposable code for fast prototyping, and clean code for scalable products Strong communication skills and ability to work collaboratively across disciplines Nice to have: Experience gathering user feedback directly and iterating based on insights Exposure to rapid prototyping methodologies and shipping MVPs Interest or experience in product design and product thinking Contributions to open-source GenAI projects or tools Familiarity with serverless architectures or edge computing Prior experience as a technical lead or architect on teams delivering AI-driven products Knowledge of advanced architecture patterns (microservices, event-driven architectures, datastore design) Experience with large-scale system design and high-performance cloud infrastructures Familiarity with governance, compliance, and security aspects in AI application architecture 10,000 - 10,000This is a great opportunity for someone in transition and looking to gain awesome building experience working alongside Andrew Ng. We are only considering candidates within commuting distance from Mountain View. As part of the interview process, you will be asked to complete a 15 minutes AI assessment.