Member of Technical Staff
AI Field Engineers at Fireworks embed with customers and technology partners to turn complex AI problems into production systems. They build POCs, MVPs, and production integrations, ship code, run benchmarks, debug production issues, and architect deployments. They also lead discovery conversations, align stakeholders, and translate customer pain points into product improvements. The role involves spending time on-site with customers to build relationships and trust. Responsibilities include building end-to-end POCs and MVPs with customer engineering teams, architecting inference foundations and sizing deployments for GenAI core products, running load tests to establish performance baselines, tuning deployments, deploying and validating new model families, guiding customers on model selection and fine-tuning strategies, building fine-tuning pipelines, designing evaluation frameworks, leading discovery conversations, owning technical relationships from first engagement to production deployment, and feeding customer signals back into the product roadmap. They also codify repeatable deployment patterns and contribute to internal tooling, documentation, and platform improvements.
AI Field Engineer - Microsoft Foundry
AI Field Engineers at Fireworks embed with customers and technology partners to turn complex AI problems into production systems quickly. They build POCs, MVPs, and production integrations, participate in executive-level discussions about architecture, strategy, and business outcomes. Responsibilities include shipping code, running benchmarks, debugging production issues, architecting deployments, leading discovery conversations, aligning stakeholders, and translating customer pain points into product improvements. They work on technical delivery and deployment by building end-to-end POCs and MVPs inside customer codebases and infrastructure, architecting inference foundations, sizing deployments for scale, running load tests, and tuning deployments to meet latency, throughput, and cost targets. They deploy and validate new model families on inference frameworks, determining optimal configurations and serving patterns. They guide customers in model selection, fine-tuning strategy, and evaluation methodology, build and run fine-tuning pipelines, and design evaluation frameworks for production metrics. They also manage customer engagement by leading discovery conversations, owning the technical relationship, embedding with customer engineering teams on-site, and building trust in person. Lastly, they provide product feedback by identifying recurring pain points, proposing product improvements, codifying deployment patterns, contributing to internal tooling and documentation, and feeding customer signals back into the product roadmap with specificity and urgency.
Director, Revenue Strategy & Analytics
As an AI Field Engineer, responsibilities include embedding with customers and technology partners to convert complex AI problems into production systems quickly. The role involves hands-on development by building proofs of concept (POCs), minimum viable products (MVPs), and production integrations. Duties comprise shipping code, running benchmarks, debugging production issues, and architecting deployments. Leading discovery conversations, aligning stakeholders, and translating customer pain points into product improvements are part of the role. Specifically, the engineer builds end-to-end POCs and MVPs inside customer codebases and infrastructure, architects inference foundations for GenAI core products, sizes scalable deployments, runs load tests to establish performance baselines, tunes deployments, and deploys models on inference frameworks while optimizing configurations. The role also includes guiding customers on model selection and fine-tuning strategies, building fine-tuning pipelines, designing evaluation frameworks, and leading engagements to embed deeply with customer teams. Field Engineers spend time on-site to build trust, identify recurring customer pain points, translate these into product proposals, codify deployment patterns to contribute back to internal tooling and platform improvements, and feed customer feedback into the product roadmap with specificity and urgency.
Paid Growth Marketer
AI Field Engineers at Fireworks embed with ambitious customers and technology partners to turn complex AI problems into production systems quickly. They build proofs of concept (POCs), MVPs, and production integrations by shipping code, running benchmarks, debugging production issues, and architecting deployments. They lead discovery conversations, align stakeholders, and translate customer pain points into product improvements, compressing the feedback loop from field to roadmap. The role involves being on-site with customers to build strong relationships and trust. Responsibilities include building end-to-end POCs and MVPs alongside customer engineering teams within their codebases and infrastructure; architecting inference foundations for GenAI core products and sizing deployments for scalability; running load tests and tuning deployments for latency, throughput, and cost targets; deploying and validating new model families on inference frameworks, optimizing shapes, quantization, and serving patterns; guiding customers on model selection, fine-tuning strategies, and evaluation methodologies; building and running fine-tuning pipelines while balancing model families, compute cost, and quality targets; designing evaluation frameworks that measure production-quality metrics; leading structured discovery conversations to understand customer pain points and proposing solutions; owning the technical relationship from first engagement through deployment; spending time on-site embedding with customers; identifying recurring customer pain points and translating them into product proposals; codifying repeatable deployment patterns and contributing to internal tooling and documentation; and feeding back customer signals into the product roadmap with specificity and urgency.
Siena - Fullstack Engineer
As a Senior Full Stack Engineer at Siena, you will own meaningful parts of the platform end to end, taking ambiguous problems and working out the right approach to ship reliable, high-performance systems that real brands depend on every day. You will own features and systems across the full stack including frontend, backend, and infrastructure from problem definition through production. You will 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 to the team, and integrate cutting-edge language models into enterprise customer workflows where reliability and safety are critical. You will solve hard engineering problems including API performance, microservices, and scaling across channels and brands. Additionally, you will maintain and improve AWS infrastructure with a DevOps mindset and contribute to raising the bar around you by sharing knowledge, reviewing thoughtfully, and helping teammates level up.
Member of Technical Staff (Machine Learning Engineer)
Translate cutting-edge research into production-ready machine learning systems. Design, build, and deploy end-to-end ML models and pipelines. Develop and optimize models for image and video processing. Own the full ML lifecycle including experimentation, training/fine-tuning, evaluation, and deployment. Rapidly prototype using open-source models and adapt them for product needs. Conduct experiments, analyze results, and iterate to improve performance. Collaborate with researchers and cross-functional teams (product, engineering, design) to deliver ML solutions at scale. Participate with advancements in machine learning and apply them to continuously improve products.
AI Deployment Engineer
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.
Engineer in Residence - Generative AI
Drive rapid engineering efforts to build full-stack applications using Generative AI tools, enabling deep exploration of product ideas, user experiences, and technical feasibility. Apply Generative AI building blocks such as prompt engineering, graph databases, vector databases, agentic frameworks, evaluations, 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 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 including AWS, GCP, and 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.
Senior AI Agent Engineer (Intelligence Service)
The Senior AI Agent Engineer on the Intelligence Service team is responsible for designing and refining the RAG-based agent flow of an interactive knowledge agent, covering the process from query understanding to planning, tool routing, retrieval, and response generation. They optimize multi-turn conversation understanding and retrieval linkage, implement response quality control logics including grounding, answer verification, guardrails, and fallback mechanisms to defend against hallucination, and establish evaluation harnesses, regression testing, and A/B testing systems for answer quality in terms of faithfulness and relevancy. They also build backend infrastructure necessary for production operations such as API contracts, caching, configuration/prompt registry, and admin APIs. Furthermore, they analyze and improve response quality, latency, and failure cases through operational logs and quality metrics. The role includes leading design reviews and technical decision-making within the team, connecting complex problems to reusable system improvements as a senior technical pillar of the team.
Software Engineer, Agent (Dutch speaking)
Design and deliver production-grade AI agents that are highly performant, reliable, and intuitive, which are central and mission-critical to Sierra's growth across industries like finance, healthcare, and commerce. Take complete ownership and autonomy over the Agent Development Life Cycle (ADLC) from initial pilot through deployment and continuous iteration, including building, tuning, and evolving AI agents in production environments while defining best practices for ADLC. Partner with leaders at large enterprises and cutting-edge startups to understand their business challenges and build AI agents that transform their operations at scale. Collaborate with customers to guide the evolution of Sierra's core platform by surfacing unmet needs, prototyping new tools and features, and working with research, product, and platform teams to shape the future of AI agent development and Sierra's product.
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