OpenAI

Manager, Forward Deployed Engineer (FDE), Life Sciences

OpenAI3 days ago
Location

San Francisco

Type

Full Time

Salary

USD 252,000 – 335,000

Level

Manager

Role

Manager

Posted

Jul 22, 2026

Full TimeManager

The role

Summary

Lead a high-impact team of Forward Deployed Engineers at OpenAI, delivering production-grade AI systems across regulated life sciences environments. This Manager role combines hands-on technical leadership with team development, requiring deep expertise in managing complex AI deployments, regulated scientific workflows, and cross-functional collaborations. You'll operate as a player-coach, balancing direct technical contributions with team growth while translating scientific and engineering challenges into measurable business outcomes.

What you'll do

Team Leadership and Development: Build, grow, and scale a high-performing team of Forward Deployed Engineers. Coach engineers through direct feedback, establish clear technical standards and execution expectations, and develop career progression pathways. Foster a culture of ownership and technical excellence while maintaining accountability for team performance and member growth trajectories.
End-to-End Delivery Management: Own complete delivery outcomes across production AI system deployments in regulated life sciences environments. Balance competing priorities of scope, speed, robustness, and risk mitigation in high-stakes engagements. Manage multi-workstream projects spanning data pipelines, workflows, infrastructure, security, and scientific stakeholder alignment.
Technical Leadership and Architecture: Serve as player-coach by directly contributing to production systems while providing technical direction and guidance. Write and review production-grade code, guide architectural decisions across backend systems and ML-adjacent infrastructure, and maintain technical credibility with both engineering and scientific teams.
Evaluation and Feedback Loops: Design and execute rigorous evaluation frameworks that measure model and system quality against workflow-specific scientific benchmarks. Convert evaluation results into crisp, actionable roadmap input for Product and Research teams. Establish metrics that demonstrate measurable workflow impact and system adoption.
Cross-Functional Collaboration: Translate complex scientific and technical tradeoffs into clear delivery plans and risk posture communications. Interface with scientific stakeholders, clinical teams, technical leads, and executive leadership. Synthesize field experience into precise feedback that shapes product strategy and go-to-market initiatives.
Production System Quality Assurance: Ensure all deployed systems meet production-grade standards in regulated environments. Establish repeatability of deployment patterns across life sciences customers. Implement robust testing, validation, and compliance practices throughout the deployment lifecycle.

What we look for

Technical

Production Code Writing and ReviewAbility to write, review, and critique production-grade code. Maintain hands-on technical capability to contribute directly to systems while maintaining management responsibilities. Comfortable in multiple paradigms including backend services, data systems, and ML infrastructure.
Backend and Systems ArchitectureDeep understanding of backend system design, scalability considerations, and distributed systems patterns. Capable of guiding architectural decisions and technical tradeoffs across service-oriented and data-intensive systems.
Data Pipeline and ML-Adjacent SystemsWorking knowledge of data pipeline architecture, data quality frameworks, and ML infrastructure considerations. Comfortable evaluating ML model performance, deployment patterns, and integration with production systems.
API Design and System IntegrationExpertise in designing robust APIs and integrating complex systems across customer environments. Understanding of security, authentication, and data governance in integrated systems.

Education

Bachelor's Degree in Computer Science or Related FieldFormal technical education in computer science, software engineering, computer engineering, mathematics, or closely related discipline. Equivalent professional experience may substitute for formal degree.
Continuous Learning in AI and Machine LearningDemonstrated commitment to staying current with advances in AI systems, large language models, and their practical applications. Evidence of learning through courses, publications, or hands-on experimentation.

Experience

8+ Years Engineering or Technical Delivery ExperienceProven track record spanning multiple full product development cycles, technical program delivery, and systems architecture work. Experience should demonstrate progressive responsibility and increasing scope of impact.
2+ Years Managing Customer-Facing or Systems-Oriented Engineering TeamsDirect management experience building and scaling engineering teams. Demonstrated success in developing technical talent, establishing high performance standards, and delivering results through others in customer-facing or infrastructure-critical contexts.
Complex Technical Program Leadership in Regulated EnvironmentsLed sophisticated, high-pressure technical programs from prototype phase through sustained production operation. Experience managing regulatory requirements, compliance considerations, and risk mitigation in production systems.
Life Sciences R&D or Scientific Software Domain ExperienceWorking experience in or adjacent to pharma R&D, biotech operations, clinical research, scientific software development, or regulated scientific data environments. Understanding of life sciences workflows, compliance frameworks, and scientific validation requirements.

Skills

Required skills

Technical LeadershipAbility to set and enforce technical standards, guide architectural decisions, and maintain credibility with engineering teams while maintaining hands-on contribution capability.
Team Management and CoachingProven capability to recruit, develop, and retain high-performing engineers. Skilled at providing constructive feedback, setting clear expectations, and creating opportunities for growth and advancement.
Production Systems OwnershipDeep commitment to production quality and reliability. Experience managing systems in regulated or high-stakes environments with mature incident response, monitoring, and continuous improvement practices.
Cross-Functional CommunicationExceptional ability to translate between technical, scientific, and business contexts. Skilled at presenting complex technical information to diverse audiences and building consensus across organizational boundaries.
Project Delivery and Risk ManagementDemonstrated ability to own complex, multi-workstream programs from conception through production. Skilled at identifying risks, making clear tradeoff decisions, and maintaining realistic timelines and scope.
AI System Evaluation and BenchmarkingExperience designing evaluation frameworks for complex systems. Capability to establish rigorous, quantitative metrics that demonstrate real-world impact and guide product decisions.

Nice to have

Customer-Facing Deployment ExperienceBackground directly managing customer engagements, field deployments, or customer success programs. Understanding of customer dynamics, objection handling, and relationship management.
Regulated Industry ExperiencePrior experience in pharmaceutical, biotech, medical device, healthcare, or other heavily regulated industries. Understanding of compliance frameworks, audit processes, and regulatory considerations.
Enterprise Software ArchitectureExperience building or deploying enterprise-grade systems emphasizing security, scalability, data governance, and integration with existing customer infrastructure.
Product Collaboration and FeedbackProven track record translating field insights into product strategy. Experience working closely with Product and Research teams to shape roadmaps based on real-world deployment learnings.
Scientific Computing and Data ScienceFamiliarity with scientific computing libraries, data analysis workflows, or computational biology. Understanding of how ML systems integrate into scientific discovery processes.
Startup or Scale-Up ExperienceBackground in fast-growing technical organizations where you've navigated ambiguity, worn multiple hats, and helped establish processes at scale.

Compensation & benefits

Salary

USD 252,000 – 335,000 (annual)

Stock options

Available

Benefits

Comprehensive Health Coverage

Medical, dental, and vision insurance with competitive premiums and coverage levels for you and your family. Mental health services and wellness programs included.

Equity and Stock Options

Meaningful equity stakes in OpenAI providing long-term wealth accumulation opportunity. Vesting schedules structured to align with company growth and your tenure.

Unlimited Paid Time Off

Flexible paid time off policy supporting work-life balance and well-being. Additional paid holidays and company-wide shutdown periods.

Professional Development and Learning

Budget and time for professional development, conferences, and continued learning. Access to educational resources, training programs, and mentorship opportunities.

Relocation Assistance

Comprehensive support for relocating to San Francisco Bay Area including moving assistance, temporary housing, and spousal placement resources.

401(k) Retirement Planning

Competitive 401(k) matching program with financial planning resources and retirement counseling support.

Hybrid Work Flexibility

Three days per week in-office requirement with two days remote work flexibility. Ability to balance collaboration and focused work from home.

Life and Disability Insurance

Company-paid life insurance and short-term/long-term disability coverage protecting you and your family.

Interview process

  1. 1
    Recruiter Screen — Initial conversation with OpenAI recruiting team to assess background, motivation, and alignment with role requirements. Discussion of career trajectory, management experience, and interest in life sciences domain. Typical duration: 30 minutes.
  2. 2
    Technical Leadership Assessment — Deep-dive conversation with engineering leader or current FDE team member. Focus on technical decision-making, code quality standards, architecture thinking, and hands-on engineering contributions. Expect discussion of specific technical challenges managed and problem-solving approach. Duration: 60 minutes.
  3. 3
    Team Leadership and Coaching Interview — Conversation with engineering manager or senior leader evaluating team-building capabilities, coaching philosophy, and people development approach. Discussion of specific examples of recruiting, developing talent, and handling difficult team dynamics. Duration: 60 minutes.
  4. 4
    Customer and Delivery Simulation — Scenario-based discussion presenting a complex, ambiguous customer deployment scenario with competing priorities, technical tradeoffs, and stakeholder considerations. Evaluate ability to decompose problems, manage risk, and communicate with diverse audiences. Duration: 60 minutes.
  5. 5
    Life Sciences Domain and Product Feedback Interview — Conversation with Product or Research leadership exploring experience in regulated environments, scientific workflows, and ability to translate field insights into product strategy. Discussion of how to bridge technical and scientific perspectives. Duration: 45 minutes.
  6. 6
    Peer and Collaboration Discussion — Conversation with potential peer managers or cross-functional collaborators assessing working relationship compatibility, communication style, and ability to influence across organizational boundaries. Duration: 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/TypeScriptSQL
Frameworks
FastAPI or DjangoReact or VuePyTorch or TensorFlow
Databases
PostgreSQLVector DatabasesData Warehouses (Snowflake, BigQuery)
Tools
Git and Version ControlCI/CD Pipelines (GitHub Actions, GitLab CI)Monitoring and Observability ToolsDocker and Kubernetes
Other
Large Language Models and Generative AIAPI Integration and Data GovernanceEvaluation Frameworks and BenchmarkingCloud Platforms (AWS, GCP, Azure)

Interview Guides

5 guides available for OpenAI

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