OpenAI

Technical Deployment Lead, Forward Deployed Engineering (FDE) - NYC

OpenAI4 months ago
Location

New York City

Type

Full Time

Salary

USD 198,000 – 294,000

Level

Senior

Role

Lead

Posted

Mar 12, 2026

Full TimeSenior

The role

Summary

OpenAI's Forward Deployed Engineering team seeks a Technical Deployment Lead to own end-to-end delivery of complex, large-scale AI systems to enterprise customers in NYC. This high-autonomy role requires deep technical project management expertise, customer partnership skills, and proven success leading transformational AI/LLM deployments across multiple interdependent work streams. You'll translate business outcomes into technical roadmaps, embed with customer teams to drive adoption, and surface reusable patterns that shape OpenAI's platform strategy.

What you'll do

Own Technical Delivery Planning: Define and execute comprehensive technical roadmaps for multiple interdependent work streams. Translate complex business objectives into actionable technical plans with clear milestones, dependency maps, and measurable acceptance criteria. Manage scope definition and ensure alignment across all stakeholders.
Lead Day-to-Day Engineering Execution: Drive daily delivery progress across OpenAI Forward Deployed Engineering (FDE) teams and customer engineering organizations. Track work streams, unblock bottlenecks, sequence tasks strategically, and make real-time trade-off decisions to protect critical path delivery without sacrificing quality or customer outcomes.
Embed with Customer Teams for Production Deployment: Work directly with customer organizations to map business workflows, understand integration requirements, and shape technical solutions that align with their operational needs. Lead production deployment execution, onboarding, adoption initiatives, and change management to ensure systems become critical to customer workflows.
Partner with Product and Research Organizations: Collaborate closely with OpenAI Product and Research teams to ensure platform components and research deliverables align with deployment timelines and customer requirements. Surface field signals and customer needs that inform roadmap prioritization and architectural decisions.
Codify Solution Patterns and Evaluation Frameworks: Extract reusable architectural patterns, integration approaches, and evaluation methodologies from customer deployments. Document and package these patterns to improve scalability and accelerate future deployments across the customer base.
Measure and Communicate Impact: Establish impact hypotheses, baseline metrics, and key performance indicators (KPIs) for customer deployments. Conduct pre- and post-deployment measurement, analyze ROI, and communicate quantified business value to executive sponsors and stakeholders.

What we look for

Technical

System Architecture and DesignStrong technical fluency in distributed systems architecture, microservices patterns, API design, and integration architectures. Ability to evaluate trade-offs, pressure-test designs, and make informed decisions about technical approaches.
AI/LLM Technical FundamentalsDeep understanding of large language model capabilities, limitations, integration patterns, prompt engineering, fine-tuning approaches, and production deployment considerations. Experience with LLM-based applications and their real-world performance characteristics.
Production System OperationsKnowledge of production deployment patterns, monitoring, observability, incident response, and reliability engineering. Experience managing systems through scale and understanding operational constraints that impact customer deployments.
Integration and Workflow MappingAbility to analyze customer business workflows, map technical requirements, design integration points, and translate operational needs into technical specifications. Skilled at identifying bottlenecks and designing systems that enhance rather than disrupt customer processes.

Education

Bachelor's Degree in Computer Science or Related FieldFormal education in Computer Science, Software Engineering, or related discipline providing foundational knowledge of computing systems, software architecture, and engineering principles. Equivalent professional experience may substitute in demonstrated cases.
Product and Business AcumenUnderstanding of product development methodologies, business strategy, customer success metrics, and how technology delivery connects to business outcomes. Experience translating between technical and business perspectives.

Experience

Customer-Facing Technical Leadership7+ years of hands-on experience in customer-facing technical delivery leadership roles, with proven success managing large-scale, high-stakes engagements that required tight cross-functional coordination and rapid decision-making under pressure.
AI/LLM Systems DeploymentDemonstrated track record shipping production-grade AI and large language model systems from concept through adoption. Strong understanding of solution architecture patterns, common integration pitfalls, performance optimization, and deployment best practices in AI/ML contexts.
Complex High-Ambiguity Project ManagementProven ability to lead transformational customer projects where scope evolves, technical unknowns exist, and business requirements shift. Experience simplifying ambiguous problems, breaking down complexity, and moving from concept to shipped systems rapidly.
Sector ExpertiseDeep expertise in at least one major enterprise sector such as healthcare, energy, financial services, semiconductors, or IT operations. Understanding of sector-specific workflows, compliance requirements, and business drivers to credibly partner with customer leaders.

Skills

Required skills

Technical Project ManagementExpert-level skill in managing complex technical projects with multiple dependencies. Expertise in roadmapping, milestone definition, dependency management, risk identification, and coordinating across distributed teams.
Customer Partnership and CommunicationExecutive presence and ability to build trust with senior customer leaders. Skill in translating complex technical concepts into business language, managing stakeholder expectations, and maintaining alignment across organizational boundaries.
AI Systems UnderstandingComprehensive knowledge of AI and LLM system architecture, capabilities, limitations, and deployment considerations. Ability to make informed trade-offs between model performance, cost, latency, and operational constraints.
End-to-End Delivery OwnershipAccountability mindset with proven ability to own outcomes from initial customer engagement through adoption and impact measurement. Comfort with ambiguity and proven judgment in prioritizing competing demands.
Cross-Functional CollaborationStrong ability to collaborate effectively with research scientists, product managers, customer engineers, and infrastructure teams. Skill in surfacing blockers, facilitating alignment, and maintaining momentum across organizational silos.
Pattern Recognition and Systems ThinkingAbility to step back from execution details, recognize patterns across multiple engagements, and connect customer needs to scalable, reusable technical solutions that improve operational leverage.

Nice to have

Experience with Enterprise AI Adoption ProgramsBackground leading enterprise-scale AI adoption initiatives, including change management, training, and driving user adoption of new AI-powered systems and workflows.
Startup or Fast-Growth Environment ExperienceExperience operating in high-growth, fast-paced environments where priorities shift rapidly, resources are constrained, and individuals must own multiple domains. Comfort with calculated risk-taking and learning through iteration.
Consulting or Services BackgroundExperience in technology consulting, professional services, or customer success roles where you've managed complex customer engagements, built credibility with customer leaders, and delivered measurable business outcomes.
Infrastructure and DevOps KnowledgeUnderstanding of cloud infrastructure (AWS, Azure, GCP), containerization, CI/CD pipelines, and deployment infrastructure. Knowledge helpful for discussing deployment and operational considerations with technical teams.
Data Science and ML OperationsFamiliarity with machine learning workflows, model deployment, monitoring, and MLOps practices. Understanding of how ML systems differ operationally from traditional software systems.

Compensation & benefits

Salary

USD 198,000 – 294,000 (annual)

Stock options

Available

Benefits

Equity Compensation

Competitive stock option packages aligned with role impact and seniority level, providing opportunity to participate in OpenAI's upside as a leading AI research and commercialization organization.

Comprehensive Health and Wellness

Full medical, dental, and vision coverage with competitive plans. Mental health support, wellness programs, and fitness benefits to support employee wellbeing.

Flexible Work Arrangement

Hybrid work model requiring 3 days per week in NYC office with flexibility for remote work and customer-facing travel. Relocation assistance available for candidates moving to the New York area.

Professional Development

Learning stipends, conference attendance budgets, and professional certification support to enable continuous growth and skill development in AI systems and deployment methodologies.

Retirement Planning

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

Paid Time Off

Generous paid vacation, sick leave, and parental leave policies supporting work-life balance and family needs.

Travel and Relocation Support

Support for 25-50% required customer travel with trip planning, booking assistance, and per diem coverage. Relocation assistance for candidates joining from other locations.

Interview process

  1. 1
    Initial Screening and Recruiter Conversation — Discuss career trajectory, motivation for joining OpenAI, and alignment with the Forward Deployed Engineering mission. Recruiter assesses communication skills, customer-facing experience, and technical background relevant to AI systems deployment.
  2. 2
    Technical Leadership Assessment — Deep dive on past customer engagements, complex project execution, and technical decision-making. Discuss specific examples of how you've managed ambiguity, made trade-offs, and driven deployments to completion. Expect questions about AI/LLM systems and architectural trade-offs.
  3. 3
    Customer Impact and Problem-Solving Round — Case study or scenario-based discussion simulating real customer deployment challenges. Demonstrate ability to translate business requirements into technical plans, identify risks, and sequence execution. Show how you'd partner with customers and navigate ambiguity.
  4. 4
    Cross-Functional Stakeholder Interview — Conversations with FDE team members, researchers, or product partners to assess collaboration style, communication effectiveness, and ability to operate at the intersection of research and commercial deployment.
  5. 5
    Executive Leadership Interview — Discussion with leadership about strategic impact, how you'd shape the FDE practice, insights from customer engagements, and vision for AI adoption patterns. Focus on judgment, pattern recognition, and ability to influence at scale.
  6. 6
    Reference Checks and Offer — References from former colleagues, customers, and managers. Emphasis on delivery outcomes, customer satisfaction, team collaboration, and impact. Final offer discussion includes compensation structure, equity vesting, and start date planning.
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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
LLM Application FrameworksFastAPI or DjangoReact or Similar Frontend Frameworks
Databases
PostgreSQLVector DatabasesRedis or Caching Solutions
Tools
OpenAI APIs and Model ServicesGit and Version ControlCloud PlatformsProject Management and Collaboration ToolsMonitoring and Observability Platforms
Other
AI Safety and Responsible AI PracticesBusiness Intelligence and AnalyticsAPI Design and Integration PatternsEvaluation and Testing Frameworks for AI Systems

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