Strategic Sourcing Manager, Data Center Infrastructure, Electrical & Mechanical
OpenAI · San Francisco, CA · Cloud, IT and infrastructure · posted today
OpenAI · San Francisco, CA · posted 4 days ago
By AtiveSite · Published 07/11/2026 · Updated 09/11/2026 · Every figure is linked to its source below.
| Employer | OpenAI |
|---|---|
| Location | San Francisco, California |
| Field | Technology › Cloud, IT and infrastructure |
| Level | not stated |
| Source category | Technical Program Management, Technical Program Management |
| Posted | 2026-11-05 (4 days ago) |
| Work arrangement | A remote or flexible location is listed |
| Pay | Not published in the source listing |
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 of models. We provide a unified platform for OpenAI teams to run production Applied AI and Research training workloads.
Our work spans cloud providers, bare metal infrastructure, data centers, and the foundational services that make compute usable. We partner across Engineering, Research, Product, Capacity Planning, Finance, and Data Center Infrastructure to bring new capacity online and support OpenAI’s growing compute needs.
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.
As a Technical Program Manager for Compute Infrastructure, you will own the supply signal for all GPUs entering our fleet: a reliable view of what capacity is arriving, where and when it will arrive, and when it is expected to become usable. You will own the roadmap, requirements, delivery, and adoption of the tools, data, and platform behind that signal, partnering with Engineering to build systems that support planning and execution across OpenAI.
You will also lead programs that turn incoming supply into usable compute. Working across cloud providers, Data Center Infrastructure, and compute engineering teams, you will connect physical site readiness with the CPU, storage, network, GPU, and foundational services required to bring new sites and clusters online.
You will be part of an engineer-first TPM team, managing a broad portfolio of infrastructure programs and making dependencies, risks, and decisions clear from working teams through leadership. Your work will help teams plan with confidence, respond to supply changes, and deliver capacity on time.
Own the GPU supply signal across the fleet. Maintain a trusted view of incoming capacity across providers, sites, and clusters, including quantities, delivery forecasts, acceptance and readiness milestones, and expected dates for production use. Make changes and their downstream effects visible.
Build and scale the tools, data, and platform behind that signal. Own the roadmap, requirements, delivery, and adoption, partnering with Engineering on data models, integrations, quality checks, and planning workflows. Serve Product TPMs, Capacity TPMs, and Compute TPMs, alongside Finance, Accounting, leadership, Engineering, and Research.
Connect supply with capacity planning. Partner with Capacity Planning on supply and demand management, allocation planning, and scenario analysis. Ensure plans reflect current supply forecasts, infrastructure constraints, and the effects of early or late deliveries.
Coordinate physical readiness for new capacity. Work closely with Data Center Infrastructure and cloud service providers to align ready-for-service dates, site construction, power and cooling readiness, supply-chain convergence, equipment delivery, and commissioning. Track dependencies and drive resolution of gaps that threaten deployment.
Lead logical readiness across infrastructure resources. Coordinate CPU, storage, network, GPU, Kubernetes, and dependent compute foundation services so new clusters can support their intended workloads. Make clear which missing services or resources could delay GPU use.
Drive deployment across cloud and bare metal environments. Lead integrated plans across AWS, GCP, Azure, OCI, and other infrastructure partners, accounting for provider-specific dependencies while establishing consistent readiness criteria and handoffs.
Establish Delivery Scorecards for cloud providers and internal infrastructure teams. Track delivery commitments, forecast changes, actual delivery, readiness milestones, and time to usable capacity. Use these scorecards to identify recurring gaps, clarify recovery actions, and improve delivery predictability.
Improve the reliability and repeatability of cluster bringup. Partner with Engineering to scale automation, runbooks, validation, and deployment processes. Reduce manual coordination and the time between capacity delivery and production use.
Manage the critical paths across concurrent programs. Establish clear milestones, owners, dependencies, and escalation paths. Resolve cross-team blockers and drive decisions that keep site and cluster deployments moving.
Communicate progress, risks, and tradeoffs clearly. Give technical and business stakeholders the information they need to understand capacity availability, assess changes to the plan, and make timely decisions.
Have 5+ years of experience leading major technical programs, including compute infrastructure, hyperscaler deployments, capital projects, or similarly complex infrastructure work.
Possess a degree in a hard science or engineering discipline, or have a demonstrated track record of equivalent engineering expertise.
Are familiar with Kubernetes deployments and have worked with at least one major cloud provider—such as AWS, GCP, Azure, or OCI—to scale compute infrastructure. Experience coordinating deployments across multiple providers is valuable.
Are familiar with bare metal GPU capacity and the provisioning, inventory, and planning systems used to bring it into service.
Have worked with Capacity Planning teams on supply and demand management, allocations, constraints, and supporting tools. You understand how changes in delivery or readiness affect downstream capacity plans.
Have experience defining requirements for operational data and internal platforms. You can work with engineers to establish useful data models, improve data quality, and build tools that teams adopt and rely on.
Understand how site readiness, power and cooling, equipment delivery, commissioning, and ready-for-service milestones affect cluster deployment. You can coordinate these dependencies without losing sight of when the intended workload can run.
Have 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.
Are technically adept and comfortable working with Engineering and Research to understand dependencies, evaluate tradeoffs, and turn complex problems into executable plans.
Build simple, scalable processes and communicate clearly with both technical teams and business stakeholders. You are resourceful in ambiguous environments and follow issues through to resolution.
Are interested in and thoughtful about the impacts of AGI.
About OpenAI
OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.
For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement.
Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.
To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form. No response will be provided to inquiries unrelated to job posting compliance.
We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link.
OpenAI Global Applicant Privacy Policy
At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.
Field: Technology Sub-niche: Cloud, IT and infrastructure All jobs at OpenAI Jobs in California Jobs in San Francisco, CA San Francisco city guide California state guide San Francisco County economy
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This is a — role in the Technology field (Cloud, IT and infrastructure) at OpenAI.
We could not match this job title to one occupation in the official wage statistics, so no pay estimate is shown. Pay is not in the source listing; ask the employer for the range and compare in the 240 job niches.
Estimate for a wage earner: 2026 federal brackets and standard deduction (IRS), Social Security 6.2% up to $184,500, Medicare 1.45% (+0.9% above $200,000 single / $250,000 married), and a state rate you set; set your own state tax rate. It ignores credits, pre-tax deductions, local taxes and benefits. Sources: IRS: tax year 2026 inflation adjustments · SSA wage base per Federal Register notice 2025-19763.
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The job is in San Francisco, CA. Below is what the official statistics say about living and working there.
How San Francisco compares. Among the 145 largest places in California, San Francisco is #4 by population, #139 for homeownership (#1 = highest), #20 for remote work (#1 = most), #25 for college degrees (#1 = most). Its county, San Francisco County, is #3 of 58 counties for average pay ($174,103).
San Francisco has 830,235 residents (ACS 2020–2024), about 2.1% of the population of California. It covers 47 square miles of land, which works out to roughly 17,784 people per square mile. The median age is 40.0, 6% older than the state median (40.0 against 37.9).
The median gross rent is $2,476 a month (rent plus utilities), 22% above the state median ($2,476 against $2,036). Over a year that is 21.1% of the median household income. US housing guidance treats housing as affordable when it takes 30% of income or less, so the typical household is under that line. The median home value is $1,394,500, which is 9.9 times the median household income ($140,970); a ratio above 5 usually means buying is out of reach without savings or a second income. 38.2% of households own their home, 32% below the state average (38.2% against 55.9%). A low ownership share points to a rental-heavy market with more apartments and shorter stays.
The San Francisco-Oakland-Fremont, CA metropolitan area has 2,373,920 jobs in the BLS wage survey, and the median pay across all occupations is $74,260 a year, 46% above the national median ($74,260 against $50,980). The biggest occupation groups are office and administrative support (226,320 jobs, median $62,120); management (223,100 jobs, median $176,340); business and financial operations (207,030 jobs, median $112,960). The area is most specialised in computer and mathematical (1.9 times) and life, physical, and social science (1.8 times), where the share of workers is well above the national share. The best-paying groups with at least 5,000 jobs are management ($176,340) and computer and mathematical ($167,770). In 2026M09 the unemployment rate of San Francisco County was 4.0%. 69.6% of residents aged 16 and over are in the labour force. The city lies in San Francisco County; its jobs, pay by industry and GDP are on the county page.
Among workers, 28.6% drive alone, 18.7% use public transit, 9.3% walk, 31.0% work from home. 21.6% of workers commute 45 minutes or more, 11% above the state average (21.6% against 19.4%). Remote work is more common than in the state as a whole, which widens the set of employers a resident can work for without moving.
60.3% of adults aged 25 and over hold a bachelor's degree or higher, 63% above the state share (60.3% against 37.1%). 34.0% of residents were born outside the United States, 26% above the state share (34.0% against 27.0%). The poverty rate is 11.2%, 7% below the state rate (11.2% against 12.0%).
The warmest month is Sep, averaging 59°F (14.9°C), and the coolest is Apr at 53°F (11.6°C). About 515 mm (20 inches) of precipitation falls in a year; Dec is the wettest month (112 mm) and Jul the driest (2 mm). These values come from NASA satellite-based climatology on a grid of about 50 km, so they describe the wider area, not a weather station in the city.
Closest cities in this guide: Daly City (31 miles, rent 3% higher); Pacifica (31 miles, rent 25% higher); San Bruno (34 miles, rent 12% higher); Millbrae (36 miles, rent 32% higher). Rent differences are against San Francisco; use the Compare tab to put any two cities side by side.
Full guide to San Francisco →California guideAll jobs in San Francisco
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Places within 50 miles (straight line) of San Francisco, CA that have a guide, with what living there costs. No pay estimate is shown because the title did not match one occupation.
| Place ⇅ | Distance from the job city ⇅ | Median rent ⇅ | Median home value ⇅ | Median household income ⇅ | Commute 45+ min ⇅ | Rent as % of occupation pay ⇅ | Open jobs there ⇅ |
|---|---|---|---|---|---|---|---|
| San Francisco, CA | 0 mi | $2,476 | $1,394,500 | $140,970 | 21.6% | n/a | Jobs |
| Daly City, CA | 31 mi | $2,551 | $1,115,000 | $123,547 | 19.6% | n/a | Jobs |
| Pacifica, CA | 31 mi | $3,098 | $1,249,800 | $160,523 | 15.5% | n/a | — |
| San Bruno, CA | 34 mi | $2,773 | $1,207,500 | $141,272 | 12.2% | n/a | — |
| Millbrae, CA | 36 mi | $3,272 | $1,934,900 | $163,476 | 21.8% | n/a | Jobs |
| Brisbane, CA | 36 mi | $2,574 | $1,232,200 | $156,169 | 29.8% | n/a | — |
| South San Francisco, CA | 36 mi | $2,802 | $1,188,800 | $136,578 | 15.2% | n/a | Jobs |
| Half Moon Bay, CA | 37 mi | $2,433 | $1,549,500 | $157,884 | 26.7% | n/a | — |
| Burlingame, CA | 38 mi | $2,785 | $2,000,001 | $174,000 | 23.1% | n/a | Jobs |
| Richmond, CA | 40 mi | $1,960 | $668,200 | $95,391 | 28.8% | n/a | — |
| San Mateo, CA | 41 mi | $3,077 | $1,618,700 | $153,504 | 15.8% | n/a | Jobs |
| Berkeley, CA | 41 mi | $2,133 | $1,413,900 | $108,092 | 20.8% | n/a | Jobs |
| Alameda, CA | 42 mi | $2,474 | $1,235,700 | $137,697 | 27.3% | n/a | Jobs |
| Belmont, CA | 43 mi | $2,875 | $2,000,001 | $205,297 | 18.6% | n/a | Jobs |
| Oakland, CA | 44 mi | $1,979 | $929,900 | $101,600 | 21.9% | n/a | Jobs |
| Foster City, CA | 44 mi | $3,501 | $1,838,200 | $199,143 | 20.6% | n/a | Jobs |
| San Carlos, CA | 45 mi | $2,854 | $2,000,001 | $237,470 | 14.9% | n/a | Jobs |
| Redwood City, CA | 47 mi | $2,968 | $1,801,700 | $157,814 | 10.2% | n/a | Jobs |
| San Leandro, CA | 48 mi | $2,186 | $844,200 | $101,420 | 23.7% | n/a | — |
| Atherton, CA | 49 mi | $3,501 | $2,000,001 | $250,001 | 7.0% | n/a | — |
We could not match this title to one occupation, so here are the other sub-niches in Technology, where your skills may carry over:
Cybersecurity (713) Data, analytics and AI (3366) Software engineering (4089) Product management (836)
How far San Francisco, CA is from the 20 largest US cities, in straight-line miles (not driving distance). Click a column to sort.
| From ⇅ | Straight-line distance (miles) ⇅ | Population of that city ⇅ |
|---|---|---|
| San Francisco, CA | 0 mi | 830,235 |
| San Jose, CA | 73 mi | 990,138 |
| Los Angeles, CA | 364 mi | 3,857,263 |
| San Diego, CA | 475 mi | 1,389,526 |
| Phoenix, AZ | 678 mi | 1,642,323 |
| Seattle, WA | 684 mi | 754,195 |
| Denver, CO | 986 mi | 718,877 |
| Oklahoma City, OK | 1,420 mi | 697,125 |
| Fort Worth, TX | 1,484 mi | 963,194 |
| Dallas, TX | 1,515 mi | 1,307,930 |
| San Antonio, TX | 1,516 mi | 1,479,835 |
| Austin, TX | 1,530 mi | 979,539 |
| Houston, TX | 1,673 mi | 2,328,253 |
| Chicago, IL | 1,885 mi | 2,711,226 |
| Indianapolis, IN | 1,979 mi | 885,860 |
| Columbus, OH | 2,142 mi | 914,802 |
| Charlotte, NC | 2,332 mi | 903,844 |
| Jacksonville, FL | 2,403 mi | 977,670 |
| Philadelphia, PA | 2,550 mi | 1,579,706 |
| New York, NY | 2,602 mi | 8,483,844 |
| City ⇅ | Straight-line distance ⇅ | Open jobs ⇅ |
|---|---|---|
| Alameda, CA | 42 mi | 38 |
| Belmont, CA | 43 mi | 2 |
| Redwood City, CA | 47 mi | 45 |
| Palo Alto, CA | 54 mi | 298 |
| Sunnyvale, CA | 60 mi | 83 |
| Fremont, CA | 62 mi | 6 |
| Pleasanton, CA | 63 mi | 6 |
| Milpitas, CA | 66 mi | 2 |
| Campbell, CA | 67 mi | 7 |
| San Jose, CA | 73 mi | 698 |
| Capitola, CA | 79 mi | 1 |
| Sacramento, CA | 103 mi | 53 |
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San Francisco, CA. A remote or flexible location is listed; confirm with the employer which states are accepted.
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The listing does not publish pay, and we could not match the title to one official occupation.
Median rent in San Francisco is $2,476 a month and median household income $140,970 (ACS 2020–2024).
It was posted on 2026-11-05 (4 days ago). Jobs older than 90 days are not shown.
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