Technical Program Manager, Compute Infrastructure

OpenAI•San Francisco, CA

10h•$207,000 - $285,000

About The Position

The Compute Infrastructure team operates the GPU fleet and large-scale compute clusters that serve the models behind ChatGPT and the API, and supports training workloads for future models. It provides a unified platform for production Applied AI and Research workloads across cloud providers, bare-metal infrastructure, data centers, and foundational services. The Technical Program Manager owns the supply signal for GPUs entering the fleet, including a reliable view of what capacity is arriving, where and when it will arrive, and when it is expected to become usable. The role owns the roadmap, requirements, delivery, and adoption of the tools, data, and platform behind that signal, partnering with Engineering to support planning and execution across OpenAI. The TPM also leads programs that turn incoming supply into usable compute. Working across cloud providers, Data Center Infrastructure, and compute engineering teams, the role connects physical site readiness with the CPU, storage, network, GPU, Kubernetes, and foundational services required to bring sites and clusters online. As part of an engineer-first TPM team, the role manages a broad portfolio of infrastructure programs, clarifies dependencies, risks, and decisions for working teams and leadership, and helps teams plan confidently, respond to supply changes, and deliver capacity on time.

Requirements

5+ years of experience leading major technical programs, including compute infrastructure, hyperscaler deployments, capital projects, or similarly complex infrastructure work.
A degree in a hard science or engineering discipline, or a demonstrated track record of equivalent engineering expertise.
Familiarity with Kubernetes deployments and experience with at least one major cloud provider, such as AWS, GCP, Azure, or OCI, to scale compute infrastructure.
Familiarity with bare-metal GPU capacity and the provisioning, inventory, and planning systems used to bring it into service.
Experience working with Capacity Planning teams on supply and demand management, allocations, constraints, and supporting tools, including understanding how delivery or readiness changes affect downstream capacity plans.
Experience defining requirements for operational data and internal platforms, including working with engineers on useful data models, data quality, and tools that teams adopt and rely on.
Understanding of how site readiness, power and cooling, equipment delivery, commissioning, and ready-for-service milestones affect cluster deployment.
A record of owning and delivering complex technical programs across internal teams and external partners, including cloud providers, equipment suppliers, or data center infrastructure teams.
Technical aptitude and comfort working with Engineering and Research to understand dependencies, evaluate tradeoffs, and turn complex problems into executable plans.
Ability to build simple, scalable processes and communicate clearly with technical teams and business stakeholders.
Resourcefulness in ambiguous environments and willingness to follow issues through to resolution.
Interest in and thoughtful consideration of the impacts of AGI.

Nice To Haves

Experience coordinating compute infrastructure deployments across multiple cloud providers.

Responsibilities

Own and maintain a trusted GPU supply signal across providers, sites, and clusters, including quantities, delivery forecasts, acceptance and readiness milestones, expected production-use dates, and downstream effects of changes.
Own the roadmap, requirements, delivery, and adoption of the tools, data, and platform supporting supply visibility; partner with Engineering on data models, integrations, quality checks, and planning workflows.
Serve Product TPMs, Capacity TPMs, and Compute TPMs, as well as Finance, Accounting, leadership, Engineering, and Research.
Partner with Capacity Planning on supply and demand management, allocation planning, and scenario analysis, ensuring plans reflect current forecasts, infrastructure constraints, and delivery timing.
Coordinate physical readiness for new capacity with Data Center Infrastructure and cloud providers, including ready-for-service dates, site construction, power and cooling readiness, supply-chain convergence, equipment delivery, and commissioning.
Track physical-readiness dependencies and drive resolution of gaps that threaten deployment.
Coordinate logical readiness across CPU, storage, network, GPU, Kubernetes, and dependent compute foundation services so clusters can support intended workloads; identify missing resources or services that could delay GPU use.
Lead integrated deployment plans across cloud and bare-metal environments, including AWS, GCP, Azure, OCI, and other infrastructure partners, while accounting for provider-specific dependencies and establishing consistent readiness criteria and handoffs.
Establish delivery scorecards for cloud providers and internal infrastructure teams, tracking commitments, forecast changes, actual delivery, readiness milestones, and time to usable capacity; identify recurring gaps and drive recovery actions.
Improve the reliability and repeatability of cluster bringup by partnering with Engineering on automation, runbooks, validation, and deployment processes; reduce manual coordination and time from capacity delivery to production use.
Manage critical paths across concurrent programs by establishing milestones, owners, dependencies, and escalation paths; resolve cross-team blockers and drive decisions.
Communicate progress, risks, and tradeoffs clearly to technical and business stakeholders so they can understand capacity availability, assess plan changes, and make timely decisions.