Strong engineering foundation through education or relevant experience.
Experience leading complex technical programs in large-scale compute or research environments.
Practical knowledge of capacity planning and compute allocation tools.
Hands-on experience building tools or data systems for engineering or research teams.
Familiarity with Kubernetes and its impact on workload execution.
Understanding of optimization solvers for fleet-wide allocation and scheduling.
Ability to translate researcher needs into practical systems.
Responsibilities
Build tools and services in collaboration with researchers across various workloads.
Own the roadmap for demand and allocation systems, from design to ongoing improvement.
Translate research plans into actionable compute demand and resource requirements.
Partner with teams to reconcile compute demand with available supply.
Create data models and automation to enhance self-service workflows for compute management.
Collaborate with engineers on Kubernetes deployments to ensure effective resource allocation.
Identify and address systemic gaps in workload and fleet data to improve efficiency.
Benefits
Opportunity to work in a hands-on technical role with direct impact on research.
Collaborative environment with engineers and researchers.
Access to cutting-edge technology and tools.
Focus on continuous improvement and innovation in compute services.
Engagement with complex, meaningful problems in the field of AGI.
Full Job Description
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 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.
You might thrive in this role if you:
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.
Have personally built or materially contributed to tools, data systems, or automation used by engineering or research teams. You are comfortable working directly with code and operational data.
Are familiar with Kubernetes deployments and scheduling systems, and understand how resource constraints and placement decisions affect workload execution.
Understand optimization solvers and can reason about their use in fleet-wide allocation and local scheduling. You can evaluate constraints, objectives, and tradeoffs without losing sight of researcher needs.
Can work closely with researchers to understand evolving requirements and translate them into systems that are practical to build and useful in daily work.
Can take ownership of ambiguous technical problems, establish a clear path forward, and bring complex work through delivery and adoption.
Communicate technical decisions, constraints, and priorities clearly across engineering, Research, and leadership.
Are resourceful, comfortable with changing priorities, and motivated by improving both researcher productivity and compute efficiency.
Are interested in and thoughtful about the impacts of AGI.
About OpenAI
OpenAI is an artificial intelligence research laboratory consisting of the for-profit corporation OpenAI LP and its parent company, the non-profit OpenAI Inc. The company was founded in 2015 by a group of technology leaders, including Elon Musk, Sam Altman, Greg Brockman, Ilya Sutskever, and John Schulman. OpenAI's mission is to develop and promote friendly AI for the betterment of humanity. The company has developed a number of cutting-edge AI technologies, including GPT-3, a language processing system that can generate human-like text. OpenAI has received funding from a number of high-profile investors, including LinkedIn co-founder Reid Hoffman and venture capitalist Peter Thiel.
