Technical Program Manager, Research Compute & Scheduling

OpenAI, Inc.; United States; Remote; USD 150,000 - 190,000; Full time

Job summary

OpenAI, Inc. is seeking a hands‑on engineering‑focused TPM to build and own the tools, data, and platform that translate research plans and workload signals into concrete compute demand and manage allocations across the fleet.

You will work directly with researchers and engineers, prototype solutions, automate workflows, and deliver tools that teams use every day. This role involves collaborating on Kubernetes deployments and scheduling to align capacity with workload requirements.

Qualifications

• Have a strong engineering foundation, developed through relevant education or demonstrated technical experience.
• Have led and delivered complex technical programs involving large‑scale compute, distributed systems, infrastructure platforms, or research environments.
• Have practical experience with capacity planning, including supply and demand management, compute allocations, and the tools and data that support those decisions.

Responsibilities

• Own Research demand and allocation systems. Own the roadmap and delivery of the tools, data, and platform used to capture demand, support allocation decisions, and manage allocations.
• Turn research plans into actionable demand. Develop systems that translate evolving plans and workload requirements into concrete capacity needs, including resource requirements, timing, and constraints.
• Connect capacity supply and demand. Partner with Capacity Planning, Research, and infrastructure teams to reconcile demand with available supply. Build the tools and workflows needed to evaluate scenarios, manage changes, and make allocation tradeoffs visible to decision makers.
• Build reliable data and useful tools. Create data models, views, and automation that show what researchers need, what capacity is available, what has been allocated, and how it is being used. Improve self‑service workflows for requesting and managing compute.
• Connect allocations to workload execution. Partner with engineers working on Kubernetes deployments and scheduling systems so allocation plans reflect workload constraints and translate into resources that researchers can use.
• Improve allocation and scheduling efficiency. Work with engineers to evaluate optimization approaches, including solvers operating at fleet and local scheduling levels. Help define constraints and objectives, assess tradeoffs, and measure improvements in fleet utilization.
• Find and address systemic gaps. Use workload and fleet data to identify bottlenecks, unused capacity, and recurring manual work. Build durable improvements to the tools and systems behind those problems.
• Lead technical delivery across teams. Own plans, dependencies, and decisions across Research, Research Compute Services, Capacity Planning, and infrastructure engineering. Keep teams aligned on what needs to be built, how it will be adopted, and who will operate it.
• Make progress and tradeoffs clear. Give working teams and leadership a clear view of demand, allocation constraints, delivery progress, and decisions that need attention.

Job description

OpenAI, Inc. is seeking a hands‑on engineering‑focused TPM to build and own the tools, data, and platform that translate research plans and workload signals into concrete compute demand and manage allocations across the fleet.

You will work directly with researchers and engineers, prototype solutions, automate workflows, and deliver tools that teams use every day. This role involves collaborating on Kubernetes deployments and scheduling to align capacity with workload requirements.