OpenAI

Manager, Technical Deployment Leads (TDL), Forward Deployed Engineering (FDE)

OpenAIYesterday
Location

San Francisco

Type

Full Time

Salary

USD 252,000 – 335,000

Level

Manager

Role

Manager

Posted

Jul 24, 2026

Full TimeManager

The role

Summary

Lead and grow a high-performing team of Technical Deployment Leads at OpenAI's Forward Deployed Engineering division, managing end-to-end production deployments of frontier AI models with customers. This role requires 8+ years of customer-facing technical delivery leadership with strong technical fluency in system architecture, deployment complexity, and ability to operate with clarity and judgment under high-pressure, ambiguous conditions. You'll own delivery outcomes across cross-functional teams including engineers, researchers, and customer teams, while building scalable operating models and surfacing critical signals back to product and research.

What you'll do

Team Leadership and Development: Build, lead, and scale a team of Technical Deployment Leads, providing direct mentorship, performance feedback, and growth opportunities. Set clear performance bars, identify capability gaps, and develop talent through actionable coaching to ensure sustained high performance in demanding customer environments.
End-to-End Delivery Ownership: Own complete delivery outcomes for frontier AI model deployments, from scope definition through production. Establish tight coordination across Forward Deployed Engineers, researchers, and customer engineering teams to ensure technical quality, schedule adherence, and business value realization.
Technical Planning and Risk Management: Develop comprehensive deployment roadmaps with clear sequencing, risk identification, and mitigation strategies. Make technically sound decisions under pressure regarding architecture trade-offs across security, reliability, latency, cost, and scope constraints.
Signal Detection and Escalation: Identify early indicators of deployment risk or product issues emerging from customer environments and field operations. Raise critical signals with appropriate urgency to research and product teams, distinguishing between noise and actionable intelligence.
Operational Standardization: Codify successful deployment practices into reusable tools, playbooks, and processes. Create scalable operating models for field team staffing, communication cadence, decision artifacts, and clear ownership that reduce complexity while maintaining quality.
Stakeholder Coordination and Communication: Partner with customer leadership and internal stakeholders to ensure delivery plans align with business objectives. Communicate progress, risks, and decisions with clarity and speed, building and maintaining customer trust through transparent execution.
Pressure Response and Course Correction: Step in decisively when deployments encounter drift or stakes increase, resetting plans, unblocking teams, and protecting customer outcomes. Apply judgment to distinguish when immediate action is required versus when situations will resolve through normal processes.

What we look for

Technical

AI/ML Systems ArchitectureTechnical fluency in modern AI and machine learning systems architecture, including understanding of model deployment complexity, inference optimization, and production ML operational requirements. Ability to pressure-test technical designs and evaluate architectural trade-offs.
Distributed Systems and InfrastructureStrong understanding of distributed systems architecture, cloud infrastructure, microservices design patterns, and their implications for system reliability, scalability, and operational complexity at production scale.
Systems Design Trade-Off AnalysisAdvanced capability to evaluate complex technical trade-offs across security, reliability, latency, cost, and functional scope. Ability to sequence work appropriately and identify dependencies across technical components and team capabilities.
Production OperationsHands-on understanding of production operations including monitoring, observability, incident response, rollback procedures, and the operational burden of various architectural choices on ongoing support and customer experience.

Education

Bachelor's Degree in Computer Science or Related FieldStrong foundation in computer science fundamentals, software engineering principles, or related technical discipline. The educational background supports deep technical conversations with engineering teams.

Experience

Customer-Facing Technical Leadership8+ years of hands-on customer-facing technical delivery leadership, demonstrating proven ability to own outcomes across complex projects and stakeholder relationships. Experience translating customer business objectives into executable technical delivery plans.
Technical Team Management2+ years managing high-performing technical delivery, program management, or customer engineering teams. Experience building team culture, setting performance standards, and developing individual contributors into strong leaders.
Production Deployment LeadershipTrack record leading complex technical projects from prototype through production at scale, managing transition from research phase through operationalization with customer handoff and ongoing support.
High-Pressure Decision MakingDemonstrated ability to simplify complex technical and business problems, making sound decisions quickly under ambiguity and pressure while maintaining clarity and psychological safety for teams.
Operating Model DevelopmentExperience building and scaling delivery operating models from the ground up, including staffing models, communication cadences, decision-making artifacts, risk management frameworks, and clear ownership structures.

Skills

Required skills

Technical LeadershipAbility to lead technical teams, make architecture decisions, pressure-test designs, and elevate technical execution through clarity rather than process overhead.
Program Management and PlanningExpert-level ability to develop comprehensive delivery roadmaps, identify risks, sequence work across multiple streams, manage dependencies, and maintain schedule adherence under pressure.
Cross-Functional CoordinationExcellence at building alignment across distributed teams including engineers, researchers, product managers, and customer stakeholders with competing priorities and different communication styles.
Customer PartnershipStrong customer-facing skills including active listening, expectation setting, transparent communication, and ability to build trust and credibility through consistent delivery and honest risk discussion.
Judgment and Decision-MakingClear judgment to distinguish between signals requiring immediate action versus normal variance, making sound calls under pressure and ambiguity while communicating rationale to teams.
Change ManagementAbility to navigate and lead through change, scope adjustment, and replanning when market conditions or technical realities require course correction while maintaining team confidence.
Team DevelopmentSkill in identifying talent, providing direct and actionable feedback, creating growth opportunities, and building psychological safety that allows teams to operate at high performance under pressure.
Technical CommunicationAbility to translate between technical deep-dives and executive-level summaries, communicating complex technical concepts clearly to non-specialist audiences without losing accuracy.

Nice to have

AI/ML Product Deployment ExperienceDirect experience deploying cutting-edge AI or machine learning systems to production customers, understanding the unique operational and technical challenges of frontier model deployment.
Enterprise Customer ManagementExperience managing complex deployments for enterprise customers with high stakes, security requirements, and sophisticated technical requirements that demand both business acumen and technical depth.
Startup and Scale-Up ExperienceBackground working in high-growth, resource-constrained environments where you've built operating systems, scaling processes, and organizational structures from first principles with limited precedent.
Research-to-Production TranslationBackground working with research teams to translate research breakthroughs into production systems, understanding the translation challenges and how to maintain research rigor while meeting production requirements.
Infrastructure and DevOps ExpertiseExperience with infrastructure architecture, deployment automation, observability systems, or DevOps practices that inform understanding of production operational excellence and reliability requirements.
Crisis ManagementDemonstrated ability to lead teams through high-stakes situations including production incidents, customer crises, or high-visibility project challenges while maintaining clarity and psychological safety.

Compensation & benefits

Salary

USD 252,000 – 335,000 (annual)

Stock options

Available

Benefits

Equity and Stock Options

Competitive equity packages with stock options reflecting company ownership and long-term value creation. Standard four-year vesting schedule with one-year cliff typical at growth-stage tech companies.

Health and Wellness

Comprehensive medical, dental, and vision coverage. Mental health support, wellness programs, and fitness benefits supporting physical and mental wellbeing.

401(k) Retirement Plan

Company-sponsored 401(k) plan with employer matching contributions, supporting long-term financial planning and retirement security.

Paid Time Off

Generous paid vacation, sick leave, and personal days. Unlimited PTO policies common at senior levels, with expectation of taking meaningful time off.

Parental and Family Leave

Comprehensive parental leave programs including birth parent and non-birth parent leave. Family planning support and adoption assistance.

Professional Development

Learning budget for conferences, courses, and professional development. Internal knowledge-sharing forums and access to cutting-edge AI research and products.

Relocation Assistance

Comprehensive relocation support for candidates relocating to San Francisco, including moving costs, temporary housing, and transition assistance.

Commuter Benefits

Pre-tax commuter benefits and transit subsidies supporting sustainable transportation options to and from the San Francisco office.

Interview process

  1. 1
    Initial Screening Call — 30-45 minute conversation with recruiting team to assess background, motivation, and high-level fit. Expect discussion of your experience with customer-facing technical leadership, team management, and complex project delivery.
  2. 2
    Technical Leadership Deep Dive — 60-minute call with current TDL or FDE engineering leader to evaluate technical depth, architecture thinking, and ability to pressure-test technical decisions. Prepare to discuss a complex systems design challenge and trade-offs you've navigated.
  3. 3
    Delivery and Program Management Case Study — 60-minute interview focused on your program management approach through a hypothetical deployment scenario. Walk through how you'd structure a complex, multi-team project with ambiguity, competing priorities, and high stakes.
  4. 4
    Team Leadership and Culture Interview — 60-minute conversation with OpenAI manager or senior leader covering your approach to building high-performing teams, developing talent, managing pressure, and maintaining psychological safety. Expect behavioral questions about specific situations.
  5. 5
    Customer Perspective Interview — 45-60 minute call with current customer-facing team member or customer engineering counterpart exploring your customer partnership skills, communication style, and approach to managing expectations and building trust.
  6. 6
    Executive Leadership Conversation — 30-45 minute final conversation with VP or executive stakeholder for cultural fit, strategic thinking, and vision alignment. Expect discussion of how you'd scale the TDL program and strategic priorities.
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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
PythonTypeScript/JavaScript
Frameworks
PyTorch and TensorFlowFastAPI and FlaskKubernetes
Databases
PostgreSQLVector DatabasesCloud-Hosted Data Warehouses
Tools
Git and Version ControlCI/CD PlatformsMonitoring and ObservabilityProject Management Tools
Other
Cloud PlatformsOpenAI APIs and ModelsSecurity and Compliance Frameworks

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