Forward Deployed Engineer (FDE) - Seattle
Forward Deployed Engineer · Senior · Full Time
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Role
What you'll do.
Forward Deployed Engineers at OpenAI lead complex end-to-end production deployments of frontier large language models and generative AI systems alongside strategic customers, owning technical delivery from discovery through stable production rollout. This role requires 5+ years of full-stack engineering experience with proven expertise in building and deploying LLM-powered systems, customer-facing technical delivery, and rapid decision-making in ambiguous environments. You will bridge product innovation and customer success, directly shaping OpenAI's model and product roadmaps through field feedback while managing 50% travel and hybrid work in Seattle.
Responsibilities
- Own End-to-End Technical Delivery: Lead and own complete deployment lifecycle for frontier models in production, spanning from initial prototype and discovery phase through architecture design, implementation, testing, and stable production rollout alongside OpenAI's most strategic customers
- Build Full-Stack Production Systems: Design and implement comprehensive full-stack systems that integrate large language models and generative AI capabilities into customer workflows, delivering measurable business value while creating feedback mechanisms for continuous model and product improvement
- Customer Technical Engagement: Embed closely with customer engineering and domain teams to deeply understand their technical requirements and business objectives, guide LLM adoption, provide hands-on technical mentorship, and ensure successful integration of frontier models into production systems
- Project Scoping and Delivery Management: Define project scope, sequence delivery milestones, identify and remove technical blockers early, make data-driven trade-offs between scope, speed, and code quality, and adjust plans proactively to protect on-time delivery
- Hands-On Development: Contribute directly to production-grade code implementation for both frontend and backend systems using Python, JavaScript, and related technology stacks when technical progress or architectural clarity depends on engineering involvement
- Operationalization and Knowledge Sharing: Codify successful deployment patterns, methodologies, and best practices into reusable tools, playbooks, and building blocks that scale across the organization and enable other engineers to execute deployments efficiently
- Field Research and Feedback Loop: Gather production insights and behavioral data on LLM model performance, identify gaps where models underperform, synthesize customer feedback, and communicate findings to Research and Product teams to directly influence model improvements and product roadmap priorities
- Cross-Functional Collaboration: Work collaboratively with Product, Research, Partnerships, Security, GRC, and GTM teams to align deployment strategy with company priorities, address compliance requirements, and drive customer success metrics that influence strategic direction
- Stakeholder Communication and Leadership: Provide clear, concise communication to internal engineering teams, product stakeholders, and customer leadership; maintain team momentum through excellent follow-through, decisive action under pressure, and calm judgment when stakes are high
Qualifications
What we look for.
Technical
Python Development
Strong proficiency in Python for backend system development, data processing, and API implementation; ability to write clean, maintainable, and performant production code
JavaScript/Full-Stack Web Development
Solid experience with JavaScript, modern web frameworks, and full-stack development patterns for building user-facing applications that integrate AI capabilities
LLM Integration and Prompt Engineering
Technical understanding of large language model APIs, prompt design, fine-tuning considerations, context management, and techniques to optimize model output for production use cases
System Design and Architecture
Ability to design scalable, reliable distributed systems; experience with microservices, API design, authentication, caching, and production deployment patterns
DevOps and Production Deployment
Experience with containerization, cloud platforms, CI/CD pipelines, monitoring, logging, and operational best practices required to run systems reliably in production
Generative AI Application Patterns
Understanding of production patterns for AI applications including RAG (Retrieval-Augmented Generation), fine-tuning workflows, evaluation frameworks, and techniques for managing model uncertainty and hallucinations
Education
Bachelor's Degree in Computer Science or Related Field (Preferred)
Formal education in computer science, software engineering, mathematics, physics, or equivalent practical engineering experience preferred but not strictly required; 5+ years of professional engineering experience can substitute
Experience
5+ Years Full-Stack Engineering Experience
Demonstrated track record of 5 or more years building and shipping production software systems with substantial experience in customer-facing technical roles that required direct customer interaction and delivery accountability
Complex Systems Delivery in Fast-Moving Environments
Proven experience scoping, architecting, and delivering complex technical systems in fast-moving, ambiguous, or startup-like environments where requirements evolve and rapid adaptation is essential
LLM and Generative AI Systems Experience
Hands-on experience building, deploying, or operating systems powered by large language models or generative AI, with deep understanding of how model behavior, output quality, and inference characteristics directly impact product experience and user adoption
Production-Grade Code Ownership
Extensive experience writing, reviewing, and maintaining production-grade code across frontend and backend domains, with ability to drive technical excellence and architectural best practices
Skills
Required
Full-Stack Software Architecture
Design and implement complete system architectures spanning frontend, backend, databases, and deployment infrastructure for production LLM-powered applications
Large Language Model Systems
Deep technical expertise in building production systems that leverage large language models, including API integration, prompt optimization, context management, and evaluation frameworks
Python and Backend Development
Professional-grade Python development for building scalable backend systems, APIs, and data processing pipelines
Customer-Facing Technical Leadership
Ability to partner directly with customer engineering teams, translate business requirements into technical solutions, and maintain accountability for production outcomes
Rapid Decision-Making Under Ambiguity
Skill in scoping work, making fast and sound technical decisions with incomplete information, and adjusting course while maintaining delivery momentum
Production Systems Delivery
End-to-end ownership of complex technical projects from design through stable production deployment with measurable business impact
Stakeholder Communication
Clear and effective communication with diverse audiences including customer stakeholders, engineers, product managers, and research teams
Preferred
JavaScript and Modern Web Frameworks
Nice to haveExperience with React, Vue, or other modern frontend frameworks for building user interfaces that integrate generative AI capabilities
Cloud Platforms and DevOps
Nice to haveHands-on experience with AWS, GCP, or Azure for deploying, monitoring, and scaling production applications
API Design and Integration
Nice to haveExperience designing and consuming APIs at scale, including error handling, rate limiting, and integration patterns for third-party services
Observability and Monitoring
Nice to haveProficiency with monitoring, logging, and observability tools (Datadog, Prometheus, etc.) for maintaining visibility into production system health
Agile and Iterative Development
Nice to haveExperience working in rapid iteration cycles with frequent customer feedback and ability to prioritize ruthlessly
Security and Compliance
Nice to haveUnderstanding of security best practices, compliance requirements (SOC 2, HIPAA, etc.), and secure software development practices
Machine Learning Operations
Nice to haveFamiliarity with ML ops, model evaluation frameworks, A/B testing for AI systems, and techniques for managing model performance in production
Tech stack
Languages
Frameworks
Databases
Tools
Other
Compensation
Pay and benefits.
Base·USD 185,000 – 300,000
Equity·Stock options
Benefits
Hybrid Work Flexibility
Flexible hybrid work model requiring 3 days in office per week in Seattle headquarters, allowing remote work flexibility for 2 days
Relocation Assistance
Comprehensive relocation package to support employees relocating to Seattle from other locations
Frontier AI Access
Hands-on access to cutting-edge AI models and research breakthroughs from OpenAI, providing unique opportunity to work with state-of-the-art technology
Strategic Customer Partnerships
Direct partnerships with OpenAI's most strategic customers, providing high-impact projects and significant career visibility
Cross-Functional Collaboration
Opportunity to work directly with Research, Product, and GTM teams to influence AI model development and product direction
Competitive Compensation
Highly competitive salary and equity package typical of top-tier AI research organizations
Professional Development
Continuous learning opportunities and exposure to frontier AI technology through direct research collaboration and customer deployments
Equal Opportunity Workplace
Commitment to diversity, inclusion, and equal opportunity employment with reasonable accommodations for applicants with disabilities
Process
Interview steps.
- 01
Initial Screening and Resume Review
Ashby ATS screening of resume and background to verify alignment with 5+ years experience requirement, LLM deployment background, and full-stack engineering credentials
- 02
Technical Phone Screen
30-45 minute conversation with hiring manager or senior engineer covering system design approach, LLM integration experience, production deployment complexity, and decision-making under ambiguity
- 03
Technical Deep Dive
60-90 minute technical interview focused on a real-world deployment scenario; may include system design questions, code review exercise, or discussion of past project architecture decisions
- 04
Customer Leadership Interview
Interview with senior customer-facing team member to assess ability to embed with customers, communicate technical concepts to non-engineers, and drive adoption
- 05
Product and Research Alignment
Conversation with Product or Research team member to evaluate understanding of LLM capabilities, model behavior considerations, and ability to translate field feedback into product insights
- 06
Final Round with Leadership
Interview with hiring manager or team lead to assess overall fit, leadership potential, decision-making under pressure, and strategic thinking aligned with OpenAI's mission
Full posting
Original listing.
About the team
OpenAI’s Forward Deployed Engineering team partners with customers to turn research breakthroughs into production systems. We operate at the intersection of customer delivery and core platform development.
About the role
Forward Deployed Engineers (FDEs) lead complex end-to-end deployments of frontier models in production alongside our most strategic customers. You will own discovery, technical scoping, system design, build, and production rollout, partnering directly with customer engineering and domain teams.
You will measure success through production adoption, measurable workflow impact, and eval-driven feedback that changes product and model roadmaps. You’ll work closely with our Product, Research, Partnerships, GRC, Security, and GTM teams.
This role is based in Seattle. We use a hybrid work model of 3 days in the office per week. We offer relocation assistance. Travel up to 50% is required.
In this role you will
Own technical delivery across multiple deployments from first prototype to stable production
Build full-stack systems that deliver customer value and sharpen how we learn
Embed closely with customer teams, understand their needs, and guide adoption of what you build
Scope work, sequence delivery, and remove blockers early
Make trade-offs between scope, speed, and quality; adjust plans to protect delivery
Contribute directly in the code when progress or clarity depends on it
Codify working patterns into tools, playbooks, or building blocks that others can use
Share field feedback that helps Research and Product understand where the models succeed and where they can improve
Keep teams moving through clarity and follow-through
You might thrive in this role if you
Bring 5+ years of engineering or technical deployment experience that includes customer-facing work
Have scoped and delivered complex systems in fast-moving or ambiguous environments
Write and review production-grade code across frontend and backend using Python, JavaScript, or comparable stacks
Have built or deployed systems powered by LLMs or generative models and understand how model behaviour affects product experience
Simplify complexity and make fast, sound decisions under pressure
Communicate clearly with engineers, product teams, and customer stakeholders
Spot risks early and adjust without slowing down
Model calm and judgment when the stakes are high
About OpenAI
OpenAI 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.
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At 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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