Full Stack Software Engineer, Codex
Build end-to-end product experiences that span frontend applications, backend services, agent workflows, cloud infrastructure, and developer tooling. Design AI-powered workflows that generalize across a wide variety of software engineering teams, languages, codebases, and development practices. Discover and implement novel ways to apply AI to eliminate friction throughout the software development lifecycle. Partner closely with product, design, and research to understand developer needs and rapidly translate insights into shipped product improvements. Work directly with users—including developers at OpenAI, open-source contributors, startups, and large enterprises—to understand pain points and validate solutions. Improve the reliability, observability, scalability, and performance of the systems and workflows you build.
AI Field Engineer - Enterprise
AI Field Engineers at Fireworks embed with customers and technology partners to turn complex AI problems into production systems quickly. Responsibilities include building POCs, MVPs, and production integrations; shipping code; running benchmarks; debugging production issues; and architecting deployments. They lead discovery conversations, align stakeholders, and translate customer pain points into product improvements. Engineers spend most of their time on-site with customers, building relationships and trust in person. They work specifically on technical delivery and deployment by building end-to-end POCs and MVPs inside customer codebases, architecting inference foundations, running load tests, tuning deployments, and deploying new model families on inference frameworks. They guide customers on model selection and fine-tuning strategies, build and run fine-tuning pipelines, and design evaluation frameworks. They engage in structured discovery conversations, own technical relationships from engagement to deployment, and spend time on-site embedded with customer teams. Finally, they identify recurring customer pain points, propose product improvements, codify deployment patterns, and feed customer signals back into the product roadmap.
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.
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.
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.
Manager, Deployment Engineering
The responsibilities include translating business requirements into requirements for AI/ML models, preparing data to train and evaluate AI/ML/DL models, building AI/ML/DL models using state-of-the-art algorithms especially transformers, testing and evaluating the AI/ML/DL models, publishing the models, datasets, and evaluations, deploying models in production by containerizing them, working with customers and internal employees to refine model quality, establishing continuous learning pipelines for models with online or transfer learning, and building and deploying containerized applications on cloud or on-premise environments.
TLM, Integrity
Architect and build next-generation system protections through hands-on design, model training, and deployment strategies. Lead and manage a small, senior team of Engineers, providing clear direction and autonomy. Collaborate with Research, Safety, Product, and Policy teams to use existing tools and advance new solutions. Utilize state-of-the-art models to detect and prevent problematic content. Establish evaluation frameworks and metrics to measure progress and identify improvement areas. Support team growth and maintain high performance through mentorship and career guidance.
Access all 4,256 remote & onsite AI jobs.
Frequently Asked Questions
Need help with something? Here are our most frequently asked questions.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.