OpenAI

Manager, Forward Deployed Engineering

OpenAI1 months ago
Location

San Francisco

Type

Full Time

Salary

USD 280,000 – 335,000

Level

Manager

Role

Engineering Manager

Posted

Jun 23, 2026

Full TimeManager

The role

Summary

Lead OpenAI's Forward Deployed Engineering team to transform AI research breakthroughs into scalable production systems for enterprise customers. As a foundational FDE manager, you'll oversee high-stakes technical deployments, drive field-informed product strategy, and build a high-performing team that operates with excellence under pressure in ambiguous, evolving environments. This role demands strategic leadership combining deep technical expertise, customer partnership skills, and the ability to translate complex field realities into actionable insights for research and product teams.

What you'll do

Lead and Scale Forward Deployed Engineering Team: Build and grow a high-performing FDE organization capable of delivering production-grade systems with frontier AI models. Recruit talent, establish team culture emphasizing excellence under pressure, set performance standards, and provide direct mentorship and feedback to elevate individual and team capability. Define team structure, roles, and growth trajectories that scale capability without organizational bloat.
Own End-to-End Technical Delivery Outcomes: Take full ownership of customer deployment success from scoping through production. Ensure technical quality, speed, and execution clarity through direct coordination with engineering teams. Make architectural decisions, resolve critical blockers, and maintain accountability for shipping timelines and business value realization.
Translate Field Intelligence into Product and Research Priorities: Serve as critical feedback channel between field deployments and OpenAI's product and research organizations. Identify patterns from customer environments, flag emerging risks or opportunities, and translate real-world deployment insights into actionable roadmap inputs. Elevate urgent signals with appropriate speed and clarity.
Build Scalable Field Delivery Playbooks and Tooling: Codify successful deployment patterns, lessons learned, and best practices into reusable playbooks, templates, and tooling. Create operational leverage that enables wider team and developer community to execute deployments with consistency and quality. Continuously refine based on field learnings and new capability.
Partner with Cross-Functional Stakeholders: Work closely with Product, Research, Sales, and Go-To-Market teams to ensure field priorities inform strategic direction, customer success stories drive new exploration, and deployment best practices support safe, scalable growth. Communicate customer needs, deployment constraints, and technical risks clearly to non-technical and technical audiences.
Drive Customer Trust and Long-Term Partnerships: Establish OpenAI as a trusted partner for enterprise customers undertaking transformational AI deployments. Lead high-stakes implementations with calm judgment and clarity. Build durable customer relationships through consistent delivery, proactive communication, and demonstrated technical excellence.
Operate with Judgment and Urgency in Ambiguous Environments: Navigate rapidly evolving AI deployment landscape with incomplete information and shifting priorities. Apply sound judgment to distinguish between urgent issues requiring immediate action and challenges that can be addressed systematically. Maintain team focus and performance despite uncertainty.
Contribute to Technical Excellence and Development: Stay hands-on with production code and technical decisions. Conduct code reviews, contribute to critical implementations, and maintain technical credibility with your team. Model engineering excellence and continuous improvement.

What we look for

Technical

Production-Grade Code Review and DevelopmentAbility to write, review, and contribute to production code across frontend and backend systems. Must maintain hands-on technical credibility while managing a team.
System Design and ArchitectureComfortable designing end-to-end system architectures from prototype through production scale. Must understand tradeoffs between performance, reliability, maintainability, and rapid deployment.
Deployment and Scaling SystemsExperience deploying complex systems to production and managing scale challenges. Understanding of infrastructure, monitoring, observability, and incident response is essential.

Education

Bachelor's Degree in Computer Science, Engineering, or Related FieldFormal degree in computer science, software engineering, or closely related discipline. Equivalent industry experience and demonstrable technical depth may substitute for formal education.

Experience

8+ Years of Engineering or Technical Delivery ExperienceMinimum 8 years building and shipping production systems, with hands-on technical contributions throughout career. Experience must demonstrate progression from individual contributor through leadership.
2+ Years Leading Customer-Facing or Field EngineersMinimum 2 years managing customer success, field engineering, solutions engineering, or similar customer-facing technical teams. Must show success building team cohesion, performance, and capability under customer-driven timelines.
High-Pressure, Prototype-to-Production Project LeadershipProven track record leading complex technical projects from early-stage prototypes through production deployment at scale. Must demonstrate ability to maintain quality and team performance under pressure and ambiguity.
Shipping Impact and Rapid IterationDemonstrated ability to ship frequently, learn from production feedback, and iterate rapidly. Track record of balancing speed with quality and owning outcomes end-to-end.

Skills

Required skills

Team Leadership and People ManagementDemonstrated ability to lead, mentor, and scale high-performing engineering teams. Must have 2+ years managing customer-facing or field engineers in complex, high-pressure environments with focus on clarity, feedback, and performance elevation.
Production System Architecture and DeliveryHands-on expertise architecting and shipping production-grade systems end-to-end. Must be comfortable owning technical decisions, code quality, and delivery outcomes from prototype through scale.
Full-Stack Development with Python and JavaScriptProficiency writing and reviewing production code across both frontend and backend systems using Python or JavaScript. Must maintain technical credibility to lead FDE teams deploying frontier AI models.
Customer-Centric Technical Problem SolvingAbility to deeply understand customer deployment challenges, translate requirements into technical solutions, and serve as trusted technical advisor during complex implementations. Must communicate clearly across technical and business stakeholders.
Strategic Decision-Making Under AmbiguityExceptional judgment to navigate complex, evolving situations with incomplete information. Must distinguish between what requires urgent action and what can wait, operating with speed and sound reasoning in high-stakes scenarios.
Cross-Functional Collaboration and CommunicationStrong ability to partner effectively with Product, Research, Sales, and Go-To-Market teams. Must translate field insights into actionable feedback and roadmap priorities while building alignment across competing priorities.

Nice to have

AI/ML Systems Deployment ExperiencePrior experience deploying machine learning models or AI systems in production environments. Familiarity with frontier model characteristics, optimization strategies, and production gotchas is valuable.
Enterprise SaaS or Developer Tools BackgroundExperience building or leading teams for enterprise-grade SaaS platforms or developer-facing tools. Understanding of scalable delivery models and developer ecosystem dynamics is beneficial.
Infrastructure and DevOps FundamentalsComfort with containerization, cloud infrastructure, CI/CD pipelines, and deployment automation. Understanding infrastructure as it relates to AI workloads is particularly valuable.
Executive Communication and Stakeholder ManagementExperience presenting technical findings to executive audiences and influencing strategy based on field signals. Ability to distill complex technical insights into executive summaries strengthens impact.
Scaling and Operational ExcellenceTrack record defining and implementing processes, tooling, and playbooks that scale team capability without adding organizational complexity. Experience with operational leverage is highly valued.

Compensation & benefits

Salary

USD 280,000 – 335,000 (annual)

Stock options

Available

Benefits

Comprehensive Health Coverage

Medical, dental, and vision insurance with company contribution toward premiums for competitive coverage.

Stock Options and Equity

Equity package aligned with OpenAI's mission and long-term value creation. Options vest over 4 years with 1-year cliff.

Flexible Work Arrangement

Hybrid work model with 3 days per week in San Francisco office. Remote flexibility for eligible candidates outside headquarters commute.

Relocation Assistance

Comprehensive support for employees relocating to San Francisco Bay Area, including moving costs and temporary housing assistance.

Professional Development and Learning

Budget and support for conferences, training, and continuous learning. Internal mentorship and coaching programs.

Generous Time Off

Unlimited paid time off policy plus paid holidays. Support for work-life balance and burnout prevention.

Wellness Benefits

Fitness stipend, mental health support, and wellness programs. On-site wellness facilities and resources.

Parental Leave

Paid parental leave benefits supporting work-life integration for new parents.

Interview process

  1. 1
    Initial Recruiter Screening — Preliminary conversation with OpenAI recruiter to assess background, experience, and alignment with role expectations. Discussion of career goals, forward deployment context, and preliminary qualification assessment. Duration: 20-30 minutes.
  2. 2
    Technical Depth Conversation — Interview with senior engineering leader covering production system architecture, technical decision-making, and hands-on coding experience. Expect discussion of past projects, system design choices, and problem-solving approaches. Prepare examples demonstrating technical leadership. Duration: 45-60 minutes.
  3. 3
    Customer Success and Field Operations Assessment — Conversation with experienced FDE or customer success leader exploring your approach to customer partnerships, deployment challenges, and field-to-product feedback translation. Discuss past customer relationships, complex deployments, and how you gather and prioritize field signals. Duration: 45-60 minutes.
  4. 4
    Team Leadership and Organizational Acumen — Conversation with existing engineering manager or organizational leader covering team building, performance management, scaling practices, and organizational decisions. Discuss your philosophy on team structure, feedback, and growing people. Explore past team scaling challenges. Duration: 45-60 minutes.
  5. 5
    Executive Stakeholder Meeting — Final conversation with senior OpenAI leadership (VP or C-level) covering strategic context, role fit, and mutual fit assessment. Discuss vision alignment, high-stakes decision-making under uncertainty, and how you approach ambiguous situations. Duration: 30-45 minutes.
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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
FastAPI or DjangoReact or Vue.js
Databases
PostgreSQLVector Databases (Pinecone, Weaviate, Supabase)
Tools
Git and GitHubDocker and Container OrchestrationCI/CD Platforms (GitHub Actions, GitLab CI, Jenkins)Cloud Platforms (AWS, GCP, Azure)Observability and Monitoring (Datadog, New Relic, Prometheus)
Other
OpenAI API and Model ArchitectureLLM Deployment PatternsDistributed Systems and MicroservicesSecurity and Compliance

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