About the Team

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

Our work connects changing research needs with capacity planning, allocation, and scheduling. We build tools and services that help researchers express their compute needs, access the right resources, and run their workloads effectively. As the fleet and our research ambitions grow, we continually improve how compute is planned, allocated, and used.

About the Role

You will be part of an engineer-first TPM team, partnering closely with the Research Compute Services team. Your primary responsibility will be 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.

This is a hands-on technical role. You will build alongside researchers and engineers, working directly with code and data to prototype solutions, automate workflows, and deliver tools that teams use every day. You will combine technical judgment and program leadership to connect capacity planning with the deployment, scheduling, and optimization systems that make allocated compute usable.

You will work across a broad range of Research workloads, understanding their different requirements and ensuring those needs shape the platform. Success means researchers can express their needs, understand their allocations, and use compute effectively as priorities and workloads change.

In this role, you will:

• Build with Research. Work directly with researchers across pretraining, research inference, and other workloads. Treat these communities as core users, and translate their needs into improvements across compute services, tooling, and scheduling.
• 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. Carry this work from requirements and design through adoption and ongoing improvement.
• 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