OpenAI

Forward Deployed Engineer (FDE) - NYC

OpenAI8 months ago
Location

New York City

Type

Full Time

Salary

USD 162,000 – 280,000

Level

Mid

Role

Forward Deployed Engineer

Posted

Nov 7, 2025

Full TimeMid

The role

Summary

As a Forward Deployed Engineer at OpenAI's NYC office, you'll lead end-to-end deployments of frontier AI models in production with strategic customers, bridging research breakthroughs and real-world applications. This role combines technical ownership of full-stack system design with direct customer partnership, requiring 5+ years of engineering experience including customer-facing deployment work and hands-on production code experience with Python, JavaScript, or comparable stacks. You'll drive measurable business impact through model adoption, workflow optimization, and feedback-driven iteration while collaborating across Product, Research, Security, and GTM teams in a hybrid environment with up to 50% travel.

What you'll do

Own End-to-End Technical Delivery: Lead complete ownership of technical delivery across multiple concurrent deployments, progressing systems from initial prototype through stable production release. This includes managing the full lifecycle of frontier model deployments, ensuring each phase achieves quality milestones while maintaining momentum and strategic customer alignment.
Build Full-Stack Production Systems: Design and implement full-stack systems that directly deliver measurable customer value, spanning frontend interfaces, backend APIs, model integration layers, and infrastructure. Your implementations must balance production-grade reliability with rapid iteration, incorporating both foundational engineering patterns and AI-specific considerations like prompt optimization and model behavior monitoring.
Embed with Strategic Customers: Work closely embedded within customer engineering and domain teams to deeply understand their technical requirements, business workflows, and adoption challenges. Translate customer needs into technical specifications, guide product adoption strategies, and collect qualitative and quantitative feedback on model performance and system usability across their organization.
Scope Work and Remove Blockers: Lead technical scoping sessions to decompose complex requirements into phased milestones, sequence delivery to maximize early value and learning, and proactively identify and remove technical and organizational blockers. Make deliberate trade-offs between scope, speed, and quality while protecting delivery timelines and maintaining stakeholder confidence.
Contribute Hands-On Code: Directly contribute production code when critical for progress, clarity, or risk mitigation. Maintain production-grade code quality across frontend and backend components, participate in code review, and serve as a technical authority on the systems you've deployed, ensuring code ownership translates to production accountability.
Codify Patterns into Reusable Tools: Systematically document and abstract successful deployment patterns, technical solutions, and customer integration workflows into reusable tools, playbooks, and building blocks. Share these across the Forward Deployed Engineering team to accelerate future deployments, reduce repetitive work, and establish best practices for LLM production deployment at scale.
Drive Product and Research Feedback: Synthesize field observations from customer deployments into structured feedback for Research and Product teams, identifying where frontier models excel and where they have fundamental limitations. Your insights directly influence model training priorities, API design decisions, and feature roadmaps, creating a closed-loop learning system.
Maintain Team Momentum Through Communication: Lead through clarity and follow-through across distributed teams, providing regular status updates, escalating risks early, and facilitating alignment between customer, internal engineering, Product, and Research stakeholders. Maintain momentum through ambiguous problems by establishing clear next steps and maintaining team confidence under pressure.

What we look for

Technical

Production Code at ScaleDemonstrated ability to write and review production-grade code across full-stack applications, with expertise in Python, JavaScript, TypeScript, or comparable backend and frontend technologies. Must have experience implementing systems serving real users with reliability and performance requirements.
LLM and Generative Model DeploymentHands-on experience building or deploying systems powered by large language models or generative models, including working with model APIs, fine-tuning approaches, prompt engineering, and understanding how model behavior, latency, and reliability affect end-user product experience and business outcomes.
Full-Stack System DesignAbility to design complete systems spanning frontend interfaces, backend services, databases, and infrastructure. Experience making architectural trade-offs, designing for scalability and reliability, and integrating third-party services or APIs into cohesive production systems.
Complex System IntegrationExperience scoping and delivering complex systems in fast-moving or ambiguous environments, with demonstrated ability to break down unclear requirements, manage technical uncertainty, and maintain quality under pressure while coordinating across multiple teams.

Education

Computer Science or Related FieldBachelor's degree in Computer Science, Software Engineering, Mathematics, Physics, or related quantitative field is strongly preferred, though equivalent professional experience and demonstrated technical depth may substitute for formal credentials.

Experience

5+ Years Engineering or Technical DeploymentMinimum 5 years of professional engineering experience with substantial portions spent on customer-facing technical delivery, including pre-sales technical consultation, implementation, and post-deployment support. Customer exposure is critical for this role.
Strategic Customer EngagementTrack record of successfully embedding within customer organizations to understand workflows, scope solutions, and drive adoption. Demonstrated ability to balance customer needs with engineering constraints and build trust with non-technical stakeholders.
Cross-Functional LeadershipExperience leading or influencing across multiple functional teams including product, research, infrastructure, and security. Proven ability to communicate complex technical trade-offs to diverse audiences and maintain alignment across organizational silos.
Decision-Making Under AmbiguityDemonstrated ability to simplify complex problems, make fast and sound decisions with incomplete information, and adjust course without panic when situations become high-pressure or stakes elevate. Experience navigating ambiguous problem spaces is essential.

Skills

Required skills

PythonStrong proficiency in Python for backend service development, data processing, and system integration. Experience with production Python stacks and popular frameworks.
JavaScript/TypeScriptSolid capability in JavaScript or TypeScript for frontend development and modern web frameworks. Understanding of both client-side and server-side JavaScript ecosystems.
System DesignAbility to design scalable, reliable systems with multiple components, making trade-offs between consistency, availability, and performance. Experience with distributed systems concepts and production deployment.
LLM APIs and IntegrationsHands-on experience integrating large language models or generative AI services into applications, including error handling, latency management, cost optimization, and prompt optimization.
Technical CommunicationExceptional ability to communicate complex technical concepts to both engineers and non-technical stakeholders, including documentation, architecture diagrams, and executive summaries.
Customer Requirements ScopingProven ability to translate ambiguous customer needs into clear technical requirements, conduct discovery sessions, and prioritize features based on customer impact and delivery feasibility.

Nice to have

React or Modern Frontend FrameworkExperience with React, Vue, or similar modern frontend frameworks for building user-facing interfaces and dashboards.
PostgreSQL and Database DesignExperience designing and optimizing database schemas, writing efficient queries, and managing data models for production applications.
Cloud InfrastructureFamiliarity with AWS, Google Cloud, or Azure for deploying and operating production systems, including containerization with Docker and orchestration patterns.
Pre-Sales EngineeringBackground in solutions engineering, technical sales support, or field engineering roles that involved demonstrating solutions to customers and scoping enterprise implementations.
MLOps and Model ServingExperience with model serving infrastructure, A/B testing frameworks, monitoring LLM outputs, and version control for machine learning systems.
Agile and Rapid IterationExperience in fast-moving startup or tech environments using agile methodologies, comfortable with continuous deployment and rapid feature iteration.

Compensation & benefits

Salary

USD 162,000 – 280,000 (annual)

Stock options

Available

Benefits

Equity and Stock Options

Competitive equity package aligned with company performance and long-term value creation, typical for AI research and deployment organizations at OpenAI's scale.

Comprehensive Health Coverage

Medical, dental, and vision insurance with coverage for employees and dependents. High-quality healthcare is a standard benefit for full-time engineering roles at OpenAI.

Retirement Planning

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

Professional Development and Learning

Access to conferences, courses, and training budgets to develop new skills and stay current with AI research and software engineering best practices.

Flexible Work Arrangement

Hybrid work model of 3 days per week in the NYC office with flexibility for remote work, allowing for focused work and office collaboration balance.

Relocation Assistance

Comprehensive relocation support for candidates moving to New York City from other locations, including assistance with housing, logistics, and transition planning.

Paid Time Off

Generous vacation and paid time off policy to support work-life balance and recovery, typical for senior technical roles in the tech industry.

Mental Health and Wellness

Access to mental health resources, wellness programs, and counseling services supporting employee wellbeing and work satisfaction.

Interview process

  1. 1
    Initial Recruiter Screening — Conversation with OpenAI recruiter to assess background, motivation, and alignment with the Forward Deployed Engineering role. Expect discussion of customer-facing experience, technical depth, and deployment philosophy.
  2. 2
    Technical Assessment — Evaluation of production engineering skills through code review of past work, technical problem-solving exercise, or system design discussion. Focus will be on full-stack capability, architectural thinking, and production-oriented mindset.
  3. 3
    System Design Interview — Deep-dive technical conversation about designing a complex system handling frontier model deployments. Expect to discuss trade-offs between reliability, latency, cost, and feature completeness in ambiguous scenarios.
  4. 4
    Customer Partnership Simulation — Scenario-based discussion simulating customer discovery and technical scoping. You'll be evaluated on ability to ask clarifying questions, understand business needs, navigate competing priorities, and communicate trade-offs effectively.
  5. 5
    LLM Product Knowledge — Conversation about your experience building with or deploying LLMs and generative models, including how model capabilities impact product design, what technical challenges you've encountered, and how you approach model evaluation in production systems.
  6. 6
    Team Collaboration and Communication — Discussion with current team members about cross-functional collaboration, communication style, and how you maintain momentum across ambiguous problem spaces. Expect questions about specific examples of technical leadership and stakeholder management.
  7. 7
    Leadership and Decision-Making — Conversation exploring how you simplify complexity, make decisions under pressure, and lead through uncertainty. You'll discuss concrete examples of high-stakes situations where you maintained team confidence and adjusted course effectively.
  8. 8
    Executive Alignment — Meeting with leadership to discuss long-term vision alignment, your understanding of frontier AI deployment challenges, and how the role contributes to OpenAI's mission of ensuring AI benefits humanity.
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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/TypeScript
Frameworks
ReactFastAPI or DjangoNext.js
Databases
PostgreSQL
Tools
Git and Version ControlDockerMonitoring and Observability Tools
Other
OpenAI APIs (GPT-4, etc.)Prompt EngineeringLLM Evaluation Frameworks

Interview Guides

5 guides available for OpenAI

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