Top Companies Hiring for Data & AI Jobs in San Francisco (Oct 2026)

You want a data or AI job in San Francisco, and you want to know who is actually hiring before you burn weeks on applications. Here is the picture as of 14 October 2026, straight from the JobRise live jobs feed: 175 open Data & AI listings in San Francisco, with 85 posted in the last 30 days and 22 in the last 7 days. These are counts of open listings, not a market survey, so treat them as a dated snapshot and recheck before you act.

The full set of openings lives in the San Francisco data jobs hub, which is where I would start each morning. Momentum matters here: 22 fresh listings in a week means the market is moving, and the roles that go quiet usually fill fast.

The companies with the most open listings right now#

Ranking by open listings, the names below carry the bulk of the volume. Note that some employers on job boards are recruiters or staffing firms posting on behalf of clients, so read each listing closely before assuming direct hire.

1. Anthropic (52)
2. Scaleai (26)
3. Figma (17)
4. Pinterest (13)
5. Lyft (10)
6. Faire (9)
7. Togetherai (8)
8. Brex (7)
9. Chime (4)
10. Asana (3)

That top-heavy shape is normal in a single city slice. Anthropic alone accounts for roughly 30% of the 175 listings, so ignoring it would be a mistake, but so would putting all your energy there. The mid-tier names like Figma, Pinterest, and Lyft are where a well-targeted application often gets more attention.

What the role titles tell you#

The titles in the snapshot skew senior and specialized. Representative openings include "Data Engineer, Billing & Payments" and "Data Engineer, Product" at Anthropic, "Manager, Software Engineering - Data Platform" at Figma, "Staff Data Scientist, Ads Product" at Pinterest, "Data Scientist, Algorithms - Dynamic Pricing" at Lyft, and "Manager, Applied AI Architecture, Enterprise Tech" at Anthropic. There is also "Staff Product Manager, Storage & Edge Data" at Fastly and "Applied AI Engineer" at Becoming, which shows the range extends past the biggest employers.

Zero of the 175 listings carry entry-level signals like intern, graduate, junior, or trainee. That is a blunt fact worth absorbing before you plan your search. If you are early career, you will need to aim slightly above your comfort zone or look at adjacent titles that use different wording.

Pay transparency is thin. Only 1 of 175 listings shows a salary, and it is "Senior Member of Technical Staff, Multimodal AI" at Cohere, posted as CA$250K, CA$535K • Offers Equity • Multiple Ranges. That is the number as posted, in Canadian dollars, and it is not a San Francisco benchmark. For context on typical ranges, cross-check the United States hiring report, and always verify current official sources since ranges move and vary by level, equity mix, and location policy.

How this city compares with the country#

San Francisco holds 175 of the 519 Data & AI listings across the United States in this snapshot. That is roughly one third of the national slice concentrated in one metro. The city also has 1,314 open listings across all categories, so Data & AI makes up a meaningful share of the local market.

What that means for you: competition is intense, but so is the density of relevant openings. You do not need to relocate to find volume. You do need to be sharper than the average applicant, because the same concentration draws talent from everywhere.

How to target each type of employer#

The list mixes product companies, fintech, marketplaces, mobility, and AI infrastructure names. Each type reads applications differently, so adjust your pitch.

• AI research and infrastructure employers, like Anthropic, Scaleai, and Togetherai: lead with models, evaluation, data pipelines, or infrastructure depth. Concrete systems and measurable results beat generalist summaries.
• Product and design-led tech companies, like Figma, Asana, and Pinterest: show how your work moved a product metric. Hiring managers here want to see collaboration with design and product, not just backend output.
• Fintech, like Brex and Chime: emphasize reliability, data quality, and regulatory awareness. Billing, payments, and risk data are recurring themes in the titles above.
• Marketplaces and mobility, like Faire and Lyft: bring pricing, forecasting, ranking, or supply-demand examples. The Lyft dynamic pricing role is a good template for the kind of problem these teams hire against.

Before you apply, run the company insights tool on the specific employer. It gives you a fast read on what the company posts and how it talks about its work, which lets you mirror their language instead of guessing.

Referral and outreach scripts that do not waste anyone's time#

Cold outreach works when it is short, specific, and easy to answer. Here are two scripts I would actually send.

Referral request to a second-degree contact:

"Hi Sam, hope you're well. I'm applying for the Data Engineer, Product role at Anthropic and noticed you spent time there. Would you be open to referring me? I've attached my resume. My background is 4 years building billing and payments data pipelines at a Series C fintech, mostly in dbt and Spark. Happy to make it easy on your end."

Cold message to a hiring manager or team lead:

"Hi Priya, I saw your team's Data Platform Manager opening and wanted to reach out directly. I led a 5-person data platform team through a warehouse migration that cut pipeline failures by 40% over two quarters. If the role is still open, I'd value 15 minutes to hear what the first six months look like. Resume attached."

Keep it under 100 words. Name the exact role. Give one proof point. Ask for something small.

A 7-step plan you can run this week#

1. Pull the top 10 employers from the list above and open their current Data & AI listings in the San Francisco data jobs hub.
2. For each company, pick one role that matches your level and paste the title into a document. Do not apply to everything at one company.
3. Run the company insights tool on each employer and write down three phrases they use to describe their work.
4. Rewrite your top three resume bullets to use those phrases where they honestly match your experience.
5. Find one contact at each target company and send the referral script. Aim for five messages, not fifty.
6. Submit applications within 24 hours of a role appearing. The 22 listings from the last 7 days are the ones with the shortest windows.
7. Track everything in a simple spreadsheet: company, role, date applied, contact, next step. Review every Friday and drop what is not working.

Here is a worked example of step 4. A weak bullet reads: "Worked on data pipelines for the payments team." The rewrite: "Rebuilt billing data pipelines in Spark and dbt, cutting nightly job failures 40% and freeing 12 engineer hours per week." Same experience, different outcome for the reader.

FAQ#

How current are these numbers?

They are a snapshot from the JobRise live jobs feed dated 14 October 2026: 175 open Data & AI listings in San Francisco, 85 posted in the last 30 days, 22 in the last 7 days. Listings change daily, so recheck the feed before you plan your week.

Are all these companies hiring directly?

Not necessarily. Some names on job boards are recruiters or staffing firms posting for clients. Read the listing body to confirm who the employer is before you invest in a tailored application.

What salary should I expect for these roles?

Only 1 of 175 listings in this snapshot shows pay, so the data cannot tell you a market rate. Typical reported ranges for senior data and AI roles in major US metros vary widely, so verify against current official postings and the hiring report before you negotiate.

I am early career. Is there anything for me?

This snapshot shows 0 of 175 listings with entry-level titles like intern, junior, or graduate. You can still apply to adjacent roles where your projects match, but widen your title search and consider nearby metros or remote listings too.

How many applications should I send per week?

Quality beats volume in a concentrated market like this. Five to eight well-targeted applications with a referral attempt each will usually outperform thirty generic ones, especially when the top employers account for so many of the openings.