OpenAI

Forward Deployed Engineer (FDE) - SF

OpenAI11 months ago
Location

San Francisco

Type

Full Time

Salary

USD 162,000 – 280,000

Level

Senior

Role

Forward Deployed Engineer

Posted

Aug 6, 2025

Full TimeSenior

The role

Summary

Forward Deployed Engineers at OpenAI lead complex end-to-end deployments of frontier AI models in production alongside strategic customers, owning the entire lifecycle from discovery through production rollout. This role requires 5+ years of engineering experience with customer-facing deployment expertise, full-stack development capabilities across Python and JavaScript, and deep familiarity with LLM systems in production environments. You'll partner directly with customer engineering teams, research, and product organizations to translate AI breakthroughs into measurable business impact while providing critical field feedback that shapes OpenAI's product and model roadmaps.

What you'll do

Lead End-to-End Technical Deployments: Own complete ownership of complex deployments of frontier AI models in production environments, managing the full lifecycle from initial prototype development through stable production rollout. Drive technical decisions, architecture design, and implementation to ensure customer success and system reliability.
Build Production-Grade Full-Stack Systems: Architect and implement full-stack solutions that deliver measurable customer value using Python, JavaScript, and modern web technologies. Balance frontend user experience with backend infrastructure requirements to create systems that are both performant and maintainable.
Drive Customer Success and Adoption: Embed directly with customer engineering and domain teams to understand their unique requirements, guide adoption of deployed solutions, and measure success through production adoption metrics and workflow impact. Translate technical capabilities into customer business outcomes.
Manage Project Delivery and Blockers: Define scope, sequence deliverables, and proactively identify and remove technical blockers. Make critical trade-offs between scope, speed, and quality while protecting delivery timelines in fast-moving environments.
Write and Contribute Code: Contribute directly to codebases when progress clarity depends on it. Write production-grade code across frontend and backend, participate in code reviews, and establish quality standards aligned with deployment requirements.
Codify Patterns and Build Reusable Assets: Document and standardize successful deployment patterns, working methodologies, and technical playbooks. Create building blocks and tools that enable other teams to replicate successes and accelerate future deployments.
Provide Strategic Product Feedback: Share real-world field observations with Research and Product teams about where frontier models succeed and where they need improvement. Translate customer feedback into actionable insights that inform product and model roadmap priorities.
Cross-Functional Collaboration: Partner closely with Product, Research, Partnerships, GRC, Security, and Go-to-Market teams. Maintain clarity and follow-through to keep cross-functional teams moving and aligned on deployment objectives.

What we look for

Technical

Full-Stack Production DevelopmentStrong proficiency writing and reviewing production-grade code across frontend and backend systems. Ability to make architectural decisions and build systems that are secure, scalable, and maintainable.
Python ProgrammingAdvanced Python development skills for building backend systems, API services, data processing pipelines, and machine learning integrations. Experience with Python frameworks and best practices for production environments.
JavaScript/TypeScript DevelopmentStrong JavaScript or TypeScript capabilities for frontend development, API integration, and full-stack applications. Experience with modern frameworks and web technologies for building responsive user interfaces.
System Architecture and DesignAbility to design scalable, distributed systems that integrate with frontier AI models. Understanding of API design, database architecture, caching strategies, and system performance optimization.
Problem-Solving Under PressureDemonstrated ability to rapidly diagnose complex technical issues, simplify ambiguity, and make sound decisions when time-sensitive problems emerge in production environments.

Education

Computer Science or Related Field (Preferred)Degree in Computer Science, Software Engineering, Mathematics, or related technical field preferred. Equivalent professional experience demonstrating mastery of core computer science concepts is acceptable.

Experience

5+ Years of Engineering and Technical Deployment ExperienceDemonstrate substantial hands-on engineering experience with significant customer-facing deployment responsibilities. Experience should include owning systems from conception through production at scale.
Complex Systems Delivery in Ambiguous EnvironmentsProven track record scoping and delivering complex, mission-critical systems in fast-moving environments with unclear requirements. Evidence of making sound decisions rapidly when specifications evolve and priorities shift.
LLM and Generative AI Systems ExperienceDirect experience building or deploying systems powered by Large Language Models or generative models. Deep understanding of how model behavior, token usage, latency, and hallucinations impact production user experience and system design.
Customer-Facing Technical LeadershipExperience working directly with customer engineering teams and technical stakeholders. Ability to translate between technical complexity and customer business objectives, and guide adoption of new technologies.

Skills

Required skills

Python DevelopmentProduction-grade Python expertise for backend systems, APIs, and integration with AI models. Should include experience with frameworks and libraries commonly used in enterprise deployments.
JavaScript/TypeScriptProficiency in JavaScript or TypeScript for building responsive frontends and full-stack applications that integrate with generative AI systems.
Full-Stack System DevelopmentEnd-to-end development capability spanning frontend, backend, databases, and infrastructure. Ability to architect solutions that work across the entire technology stack.
LLM Integration and Prompt EngineeringExperience integrating Large Language Models into applications, understanding token economics, context windows, and techniques to reliably generate desired model outputs in production.
Technical CommunicationAbility to articulate complex technical concepts clearly to both technical engineers and non-technical customer stakeholders. Skill in translating between technical details and business impact.
Project Scoping and DeliveryCapability to scope ambiguous requirements, define technical deliverables, and manage delivery timelines while making sound trade-offs between scope, speed, and quality.
Production Systems ReliabilityExperience building and maintaining systems designed for high availability, performance, and reliability. Understanding of monitoring, observability, and incident response in production environments.

Nice to have

Generative AI Application DevelopmentPrevious experience building and deploying consumer or enterprise applications powered by generative AI, including experience with prompt optimization and model behavior validation.
Cloud Platform ExpertiseHands-on experience with major cloud platforms (AWS, Google Cloud, Azure) including deployment, scaling, and cost optimization. Understanding of containerization and orchestration tools like Docker and Kubernetes.
API Design and MicroservicesExperience designing and building RESTful or GraphQL APIs, implementing microservices architectures, and managing service integration in complex distributed systems.
Customer Success and Field EngineeringBackground working directly with enterprise customers on implementation, troubleshooting, and adoption. Experience gathering field feedback and translating it into product improvements.
Startup or Hyper-Growth Environment ExperienceProven ability to thrive in environments with ambiguity, rapid change, and evolving priorities. Experience building systems in resource-constrained or fast-moving contexts.
DevOps and Infrastructure as CodeFamiliarity with infrastructure automation, CI/CD pipelines, and infrastructure-as-code practices. Experience managing deployment workflows and ensuring reliable production releases.

Compensation & benefits

Salary

USD 162,000 – 280,000 (annual)

Stock options

Available

Benefits

Stock Options and Equity

Competitive equity package providing ownership in OpenAI and upside participation as the company scales its AI capabilities and product offerings.

Comprehensive Health Insurance

Medical, dental, and vision coverage with low employee contributions. Plans cover preventive care, prescriptions, and specialist care.

Retirement Planning

401(k) retirement plan with employer matching to support long-term financial security and retirement savings.

Relocation Assistance

Full relocation support provided for candidates relocating to San Francisco, including moving expenses and housing assistance.

Flexible Work Arrangements

Hybrid work model with 3 days per week in San Francisco office, providing flexibility while maintaining collaborative culture and team connection.

Professional Development

Access to training, conferences, and learning opportunities to stay current with AI research, engineering best practices, and emerging technologies.

Wellness Programs

Health and wellness initiatives including fitness programs, mental health support, and wellness resources for employee wellbeing.

Paid Time Off

Generous paid vacation, sick leave, and personal days. Sabbatical opportunities available for long-term employees.

Travel Allowance

Support for required customer travel (up to 50% travel requirement) with consistent allowances and travel time policies.

Interview process

  1. 1
    Initial Screening Call — Preliminary 30-minute conversation with a recruiter to assess background, experience with AI systems, customer-facing deployment work, and cultural fit. This screening focuses on your experience with LLM integration and complex system delivery.
  2. 2
    Technical Phone Interview — 60-90 minute technical discussion with an engineer covering Python and JavaScript proficiency, system design principles, and experience building production systems. Expect questions about architecture decisions you've made and trade-offs you've navigated.
  3. 3
    System Design Interview — 90-minute deep-dive technical interview focusing on designing scalable systems that integrate generative AI models. You'll walk through architecture decisions, API design, database choices, and how you'd approach building systems at OpenAI's scale.
  4. 4
    Customer-Facing Scenario Interview — Technical interview simulating customer deployment scenarios. You'll discuss how you'd scope a complex requirement, identify technical blockers, make architectural trade-offs, and communicate with both technical teams and non-technical stakeholders.
  5. 5
    Code Review and Collaboration — Interactive session reviewing actual production code (similar to what you'd encounter at OpenAI) or pair programming on a realistic engineering problem. This assesses your code quality standards, communication during collaboration, and problem-solving approach.
  6. 6
    Leadership and Team Fit Discussion — 30-minute conversation with a senior engineer or manager about your approach to cross-functional collaboration, handling ambiguity, decision-making under pressure, and how you provide feedback that shapes product roadmaps. Discussion includes company culture and working at OpenAI.
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OpenAI

OpenAI

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OpenAI is an American artificial intelligence research organization developing advanced AI models like GPT. Focused on ensuring AI benefits humanity, it creates tools for natural language processing and generative AI applications.

San Francisco, California, United StatesFounded 2015openai.com

Tech Stack

Languages
PythonJavaScript/TypeScriptSQL
Frameworks
FastAPI or DjangoReact or Vue.jsNext.js
Databases
PostgreSQLRedisVector Databases
Tools
Git and Version ControlDocker and ContainerizationKubernetesCI/CD PlatformsMonitoring and Logging
Other
OpenAI API and SDKPrompt EngineeringModel Evaluation FrameworksAgile Development Practices

Interview Guides

5 guides available for OpenAI

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