Sr. Technical Program Manager, Ads Delivery Infrastructure
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Looking for a technical program manager job in San Francisco (SF)? Choose from 163 open roles at 43 employers. Explore real salary data and skills breakdown across remote, hybrid and on-site roles.
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As of 23 September, 15 technical program manager roles are open in San Francisco at 9 employers. Of the roles that say how they work, 67% are remote or hybrid.
Among the technical program manager roles listed on the board for San Francisco, the majority sit at mid level, with roughly a fifth at lead level and a small minority at senior level. About a third of the openings are older postings, and roughly a quarter arrived within the past month, with the remainder spread across the past few weeks. Hiring is notably concentrated: most roles listed here come from a small number of employers rather than being spread evenly across the market. Skills mentioned most often in descriptions lean heavily toward AI and machine learning topics — evaluation, large language models, and APIs each appear in a substantial share of postings, and GPU-related work is mentioned in roughly a quarter of them.
Figures are measured every Monday.
A technical program manager coordinates complex, cross-functional engineering efforts: setting milestones, managing dependencies, tracking risks and keeping multiple teams aligned toward shared delivery goals. The role sits between engineering leadership and product, requiring enough technical fluency to interrogate designs and unblock teams without writing production code day-to-day. In San Francisco, the concentration of AI-focused companies — including large model developers, enterprise data platforms such as Databricks, and established enterprise software firms with headquarters in the city — means that TPM work here often involves AI infrastructure, model deployment pipelines, or platform integrations. The proximity to research institutions across the Bay adds to a culture where TPMs regularly coordinate with applied research teams as well as product engineering.
Descriptions point toward a candidate who is comfortable working around modern AI systems: familiarity with large language models, APIs, and evaluation methodologies appears in a notable share of postings. GPU infrastructure and A/B testing also appear often enough to suggest they are practical, not peripheral, parts of the work. Prompt engineering and diffusion models appear in a smaller minority of descriptions, indicating that some roles extend into generative AI product development specifically.
The employers' own advertised ranges, for individual roles at different levels — not an average and not a market rate.
Just over half of the roles that state a work mode on the board are hybrid, making that the most common arrangement. About a third are fully on-site, reflecting the weight of employers with established San Francisco offices. A small minority are listed as remote. Candidates should expect most opportunities to involve some regular in-person presence.
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