AI Engineer Jobs in San Francisco, USA

Looking for an AI engineer job in San Francisco? Choose from 80 open roles at 33 employers. Explore real salary data and skills breakdown across remote, hybrid and on-site roles.

80 Open roles
33 Companies hiring
260K Average salary

What do AI engineers in San Francisco work on?

The roles on this page centre on building, deploying, and maintaining AI systems in production. Across the sample summaries, a recurring pattern is the combination of large language model inference, foundation model training, and model evaluation at scale. Capital One's Senior Staff AI Engineer typifies this: designing and deploying AI components including vector databases and LLM inference while optimising large-scale systems. The Staff AI Engineer on the Enterprise Analysis Platform adds a product dimension, building guardrails and similarity search for an internal tool serving a large user base. Titles such as Distinguished AI Engineer and Senior Director of AI Engineering suggest a wide span of seniority, while names like "Lead AI Engineer – Agentic AI, Agent Guardrails, Agent Evaluation, Agent Memory" point to a strong concentration of work around agentic systems. Manager-level postings appear across several employers, indicating that some roles carry team leadership responsibilities alongside technical delivery.

Skills and experience employers ask for

These counts reflect how often a term appears in descriptions across all postings on this page, not a formal requirement list. LLMs and evaluation surface in the clear majority of postings, and Python appears in more than half. Agents, APIs, and cloud platforms such as AWS, GCP, and Azure are each mentioned in a substantial portion. A master's degree and research publications appear in roughly a quarter of postings, suggesting some but not all roles expect an academic background. The sample covers the full set of live jobs, so the pattern is reasonably consistent.

LLMs 84%
Evaluation 78%
Python 69%
APIs 61%
Agents 55%
A/B testing 26%
PyTorch 25%
AWS 25%
GCP 24%
Azure 23%
Master's degree 23%
Publications 23%
Java 19%
C\+\+ 19%

AI Engineer roles that state a salary

The employers' own advertised ranges, for individual roles at different levels — not an average and not a market rate.

San Francisco office, hybrid or remote?

The majority of postings on this page are listed as onsite, with a meaningful minority designated hybrid. Only a small number are explicitly remote. Where a posting carries a "Remote Eligible" label in the title, that is reflected in the individual listing. No postings have an unknown work mode, so the breakdown is complete for this set. Candidates should check individual postings for the specific office location, as the sample summaries indicate at least one role is based in Cambridge, MA despite appearing in this San Francisco listing.

Make your application specific to the work

  1. Review the full job description Many summaries are truncated on this page; read the complete posting before applying, as eligibility criteria such as degree level and years of experience vary noticeably across roles.
  2. Match your experience to seniority These postings range from intern to senior director level; check that the title and the stated years of AI or ML development experience align with your background before starting an application.
  3. Prepare for technical and evaluation focus LLM inference, model evaluation, and agentic system design appear across the majority of descriptions, so be ready to discuss hands-on work in these areas; Python proficiency is also widely mentioned.
  4. Confirm work mode and location Most roles are onsite or hybrid; verify the specific office address and any remote eligibility directly with the employer, as these details are not always fully stated in the summary text.

Questions about AI Engineer jobs in San Francisco

A master's degree is mentioned in roughly a quarter of the postings on this page, and where it appears it typically reduces the required years of experience rather than being the only accepted qualification. Many postings also list a bachelor's degree as sufficient with additional years of experience. Each posting states its own criteria, so check the individual listing.

Only a minority of postings on this page advertise a pay range. Among those that do, the figures vary widely by employer and seniority; for example, some individual contributor roles at larger AI companies show upper bounds above $380,000, while other postings show lower starting points around $165,000. These are the employers' stated ranges, not market-wide figures.

A small number of postings are listed as remote. A larger share are hybrid, and the majority are onsite. Where "Remote Eligible" appears in a title, that designation is carried through to the individual posting, but the specific terms are not stated in the summaries and should be confirmed with the employer.

Visa sponsorship eligibility is not stated in the fact pack or the sample summaries for any of the postings on this page. You would need to check each posting directly or contact the employer to confirm.

Based on the descriptions on this page, the most common focus areas are large language model inference and evaluation, foundation model training, and agentic system design including agent guardrails and memory. Cloud platform deployment and API integration also appear frequently. Some roles have a strong production engineering emphasis, while others appear to combine research and engineering.

Jobs checked 6 hours ago. · Guide reviewed 9 September 2026.