Technical Program Manager, Research Compute Services

Details

About the Team

Research Compute Services builds systems that help researchers use OpenAI’s compute fleet effectively. The team partners with Research and Compute Infrastructure to support workloads across the compute stack, including pretraining, synthetic data generation, sampling, research inference, and reinforcement learning.

The work connects changing research needs with capacity planning, allocation, and scheduling. The team builds tools and services that help researchers express compute needs, access the right resources, and run workloads effectively.

About the Role

This is a hands-on technical role on an engineer-first Technical Program Management team. The role partners closely with Research Compute Services to build and own the tools, data, and platform that turn research plans and workload signals into concrete compute demand and manage allocations across the fleet.

The successful candidate will work directly with researchers and engineers, code and data to prototype solutions, automate workflows, and deliver tools used by teams every day. The role combines technical judgment and program leadership to connect capacity planning with deployment, scheduling, and optimization systems.

Responsibilities

• Work directly with researchers across pretraining, research inference, and other workloads, translating their needs into improvements across compute services, tooling, and scheduling.
• Own the roadmap and delivery of tools, data, and platforms used to capture demand, support allocation decisions, and manage allocations.
• Develop systems that translate research plans and workload requirements into concrete capacity needs, including resource requirements, timing, and constraints.
• Partner with Capacity Planning, Research, and infrastructure teams to reconcile demand with available supply.
• Build tools and workflows to evaluate scenarios, manage changes, and make allocation tradeoffs visible to decision-makers.
• Create data models, views, and automation showing researcher needs, available capacity, allocated capacity, and usage.
• Improve self-service workflows for requesting and managing compute.
• Partner with engineers working on Kubernetes deployments and scheduling systems so allocation plans reflect workload constraints and translate into usable resources.
• Evaluate optimization approaches, including solvers operating at fleet and local scheduling levels.
• Define constraints and objectives, assess tradeoffs, and measure improvements in fleet utilization.
• Use workload and fleet data to identify bottlenecks, unused capacity, and recurring manual work, then build durable improvements.
• Lead technical delivery across Research, Research Compute Services, Capacity Planning, and infrastructure engineering.
• Own plans, dependencies, decisions, adoption, and operational responsibilities across teams.
• Communicate demand, allocation constraints, delivery progress, and decisions requiring attention to working teams and leadership.

Requirements

• Strong engineering foundation developed through relevant education or demonstrated technical experience.
• Experience leading and delivering complex technical programs involving large-scale compute, distributed systems, infrastructure platforms, or research environments.
• Practical experience with capacity planning, including supply and demand management, compute allocations, and supporting tools and data.
• Experience building or materially contributing to tools, data systems, or automation used by engineering or research teams.
• Comfort working directly with code and operational data.
• Familiarity with Kubernetes deployments and scheduling systems, including how resource constraints and placement decisions affect workload execution.
• Understanding of optimization solvers and their use in fleet-wide allocation and local scheduling.
• Ability to evaluate constraints, objectives, and tradeoffs while considering researcher needs.
• Ability to work closely with researchers, understand evolving requirements, and translate them into practical systems.
• Ability to take ownership of ambiguous technical problems and bring complex work through delivery and adoption.
• Clear communication of technical decisions, constraints, and priorities across engineering, Research, and leadership.
• Resourcefulness and comfort with changing priorities.
• Interest in and thoughtfulness about the impacts of AGI.

Benefits

• Equity, performance-related bonuses for eligible employees, and comprehensive benefits.
• Medical, dental, and vision insurance, with employer contributions to Health Savings Accounts.
• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses.
• 401(k) retirement plan with employer match.
• Paid parental, medical, and caregiver leave.
• Paid time off, company holidays, office closures, and paid sick or safe time.
• Mental health and wellness support.
• Employer-paid basic life and disability coverage.
• Annual learning and development stipend.
• Daily office meals and eligible meal delivery credits.
• Relocation support for eligible employees.
• Additional taxable fringe benefits may include charitable donation matching and wellness stipends.