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Research Services👥 201 employees📍 San Francisco, CA, USEst. 2015
OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. AI is an extremely powerful tool that must be created with…
Job Overview
Technical Program Manager, Compute Infrastructure Location: San Francisco Employment Type: Full time Department: Technical Program Management Compensation: $257K – $335K • Offers Equity
About the Team
The compute infrastructure team runs the GPU fleet and large-scale compute clusters that serve the models backing ChatGPT and the API, while also supporting training workloads for our next generation models. We operate a large, modern GPU fleet and provide a unified platform for other OpenAI teams to seamlessly run production Applied AI and Research training workloads.
We seek to learn from deployment and distribute the benefits of AI, while ensuring that this powerful tool is used responsibly and safely. Safety is more important to us than unfettered growth.
About the Role
You will be part of an engineer-first TPM team as a Technical Program Manager for Compute Infrastructure who owns the end-to-end delivery of large-scale GPU clusters, partnering with engineers to bring clusters online across external providers and partners. You’ll run a broad, parallel portfolio spanning hardware, networking, power, and cooling—driving execution, risk management, and crisp alignment from working teams through leadership to deliver production-ready capacity at scale.
This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.
Key Responsibilities
Lead end-to-end delivery of both New Compute SKUs and large-scale GPU clusters across an external partner ecosystem while supporting capacity planning for training and inference.
Ability to contextually drive multi-threaded bring-up programs spanning hardware, networking, power, and cooling—owning plans, dependencies, and critical paths.
Interface with chip providers to derisk long-term onboarding to new hardware platforms by working across kernels, comms, hardware, and scheduling engineering teams.
Build and operationalize program mechanisms (roadmaps, milestones, risk registers, runbooks) that make delivery predictable at massive scale.
Partner with engineering to improve cluster turn-up reliability, repeatability, and automation, reducing time-to-serve for new capacity.
Support network operations and end-to-end physical and logical bring-up of OpenAI network Points-of-Presence (PoPs), including on-site deployment, rack cabling, and close collaboration with engineering teams.