AI Staffing San Francisco: Machine Learning, LLM and Generative AI Engineers
Placed on contract, contract-to-hire, and direct hire across the Bay Area. We recruit at the same speed the labs make offers, because that’s the only speed that wins here.
KORE1 provides AI staffing in San Francisco, placing machine learning engineers, LLM engineers, generative AI engineers and ML platform engineers on contract, contract-to-hire and direct hire, averaging 17 days to first qualified submit and 92% one-year retention.
Last updated: August 13, 2026
A Series B company on Townsend Street sent us a job description in June. Senior AI engineer, PhD strongly preferred, five years minimum with large language models.
What they actually needed was someone who could stop their support-ticket classifier from hallucinating and bring the OpenAI bill down by a third before the next board meeting. That is not a research hire. It never was.
Every hiring manager in this city is recruiting against labs whether the job posting admits it or not.
San Francisco holds the densest concentration of frontier AI labs and applied machine learning teams in the country. OpenAI, Anthropic, Google DeepMind, Meta and Scale AI all draw from the same few thousand engineers, and a startup on Folsom Street is drawing from that pool too, whether it wants to admit that or not, along with every well-funded Series A trying to hire its first applied ML lead out from under a company with a nine-figure compute budget and a recruiting team that never sleeps. The resume keywords stopped meaning anything two years ago. Everyone lists “LLM” now.
KORE1 has staffed IT roles since 2005, and our AI recruiting desk screens on shipped systems, not on the buzzwords sitting at the top of a resume. That takes longer to run than a keyword search. It is also a lot cheaper than a bad hire in a seat this expensive.
One scope note before we go further. This page covers AI, machine learning and LLM engineering roles specifically. For the pipelines and warehouses those models actually run on, start with data engineer staffing or data scientist staffing instead.
Your Competitor for This Hire Isn’t Another Staffing Agency
It’s an internal recruiting org with a name, a Slack channel, and same-day approval on comp. That is who you are actually up against for a strong LLM or ML engineer in this city, and most hiring managers plan their search as if the only competition is a slower interview loop somewhere else.
In practice, it looks like this. A candidate takes a call with your team on Monday, and by Wednesday a lab has extended an offer built mostly out of equity that resets the moment the model ships something impressive, the kind of number a finance team at a smaller company would need three separate approvals just to consider matching. Cash alone rarely wins that trade. What wins is speed, a clear scope, and an offer that does not require the candidate to guess what year three looks like.
We ask three questions before a single resume moves. What do you actually pay in cash versus equity, and can you say that number out loud on day one? What happens to the role if the model gets replaced by a better one in six months, because it will? And is the hiring manager available to close within 48 hours of a strong final round, or does that decision route through a committee that meets on Thursdays?
Companies that can answer all three close hires here. The ones that can’t usually blame the market. It’s rarely the market. It’s the clock.
Which AI Engineer Do You Actually Need
Ask this before the req goes out. It takes about a minute and it decides the seat, the salary band, and how long the search will actually run.
If your product calls a hosted model, Claude, GPT, Gemini or something running on Bedrock, and the work is retrieval, evaluation, guardrails, and the cost per request, you need an LLM engineer. This is the highest-volume AI seat in the Bay Area right now, and it is also the one most companies mistake for something rarer.
If your team trains, fine-tunes, or serves weights it owns, you need a machine learning engineer who has actually run a training job and knows what a GPU bill looks like at 2 a.m. when it climbs. If the output is image, audio, or video instead of text, that narrows further to a generative AI engineer.
Genuinely novel modeling and publication-track work is rare and it is expensive. The Bureau of Labor Statistics counted 40,300 computer and information research scientists nationally in 2024, against 245,900 data scientists the same year, a pool six times larger. Write a research req when the work is applied and you’ve entered the smaller, pricier pool for no reason. Check first. It matters.
Six Roles, and Almost Nobody Staffs All Six
Most Bay Area teams already run two of these and call KORE1 about the third. That’s exactly what a specialist desk is for.
Training, evaluation, and serving for models your team owns outright. PyTorch, feature stores, GPU scheduling. See machine learning engineer staffing.
Retrieval, context assembly, tool use, agents, eval suites, and the cost curve nobody modeled before launch day. The highest-volume AI seat in the Bay Area. See LLM engineer staffing.
Diffusion and multimodal work, image, audio and video pipelines, and the provenance questions that come bundled with them now. See generative AI engineer staffing.
Novel modeling and publication-track work. A small national pool, and in San Francisco you’re recruiting against the labs directly. See AI research scientist staffing.
The seat that decides whether anything ships a second time. Training and serving infrastructure, CI for models, rollback. See ML platform engineer staffing or our MLOps recruiters.
Owns what “good” means on an AI product, with an evaluation artifact behind that answer rather than a hunch. See AI product manager staffing.
We also place two adjacent specialties, computer vision engineers and NLP engineers, plus prompt engineers for teams that have separated that work out.
What Happens in the 48 Hours After a Lab Makes an Offer
Not what happens after your interview loop finishes. What happens the moment a candidate has two offers on the table and one of them came from a name you can’t match on cash alone.
Every hour without a real counter is an hour the candidate spends taking somebody else’s call. KORE1 averages 17 days to first qualified submit across current placements, with a 92% 12-month retention rate once a hire actually starts, and neither number moves much even when a search runs through the Bay Area’s specific mix of equity negotiations, competing lab offers, and finance teams that need three signatures to approve a comp band the market already settled weeks ago. Nationally, BLS projects 20% growth for computer and information research scientists from 2024 to 2034 and 34% for data scientists, both far above the average for all occupations, and we run searches across 30+ U.S. metros so a stalled Bay Area pipeline is a reason to widen the map, not a reason to lower the bar.
Three Bay Area Markets, Three Different Comp Conversations
The same job title means a different offer structure depending on which of these three you’re hiring into.
SOMA and the city core
South of Market, the Financial District, and the Mission. The heaviest concentration of applied LLM product work in the Bay, sitting closest to the labs themselves. Comp here runs the most equity-heavy of the three, and candidates are used to comparing offers against a lab’s own package because they usually have one in hand.
The Peninsula and Silicon Valley corridor
Palo Alto, Mountain View, Sunnyvale, San Mateo, down through San Jose. Deeper bench of ML platform and infrastructure talent, more established companies alongside the startups, and a candidate pool that skews toward people who have already shipped a model to production at least once. Onsite expectations here tend to be firmer than in the city.
Oakland and the East Bay
Oakland, Berkeley, Emeryville. A smaller AI employer base but a real one, anchored by research spillover from UC Berkeley and a growing number of applied teams priced out of San Francisco proper. Compensation runs slightly below the city core, and the tradeoff usually buys a calmer hiring process on both sides.
We recruit across all three as one connected market rather than letting a San Francisco search die at a bridge or a county line, because the candidates already do the same thing, and treating SOMA, the Peninsula, and the East Bay as three separate searches instead of one connected labor pool is the single most common reason a Bay Area req sits open past the point where it should have closed. A strong LLM engineer in Oakland will commute to SOMA for the right offer, and a Peninsula platform engineer will take a hybrid role in the city if the scope is right.
Who Carries the Risk While the Scope Is Still Moving
Same recruiters and the same network behind all three. What changes is where the uncertainty sits, and who’s holding it.
Contract & Contract-to-Hire
An engineer employed by KORE1 and embedded on your team, typically three to nine months. Right for a first production feature, where nobody honestly knows what month six looks like yet.
Contract Staffing →Direct Hire
For the platform seat and the eval-owning seat, the two roles that outlast whatever model you’re running today. That institutional memory has no market price and is brutal to replace.
Direct Hire details →Project & Statement of Work
A team we assemble and manage against deliverables you define. Fits a migration off a legacy model, a fixed launch date, or an eval program that has to exist before a board update.
Project Staffing →
The Screen We Run Before You See a Profile
Four questions, none of which can be rehearsed in the car on the way over. A candidate who has actually shipped answers all four in under two minutes. The rest hedge, and it’s usually obvious which is which.
How did you know it was working? People who shipped point to an eval set, a rubric, a score that moved. People who haven’t reach for a benchmark name.
What did it cost per request, and what did you do about it? Anyone running an LLM feature in production knows that number to the dollar, because finance asked them for it eventually.
What did you take out? Strong engineers delete things. A retrained model that got replaced by a lookup table. Weak candidates only ever describe additions.
We also ask what broke in production and how they found out. Drift monitoring and a canary alert is a good answer. A user complaint three weeks later is not. Three weeks is too long.
Common Questions
What does it cost to hire an AI engineer in San Francisco?
Across current KORE1 Bay Area placements, contract LLM and applied AI engineers bill roughly $105 to $175 an hour, machine learning engineers $120 to $195, and ML platform engineers $125 to $190. Research-track scientists run considerably higher.
Those bands move for three reasons. Model ownership matters most, because training and serving your own weights pays more than calling an API. Equity exposure at the candidate’s current job is next, since someone sitting on a fresh lab grant is harder to move on cash alone. Onsite expectations come third, and a five-day requirement on the Peninsula quietly removes a large share of the city-based candidate pool before you’ve read a single profile.
Do we need a machine learning engineer or an LLM engineer?
If your product calls a hosted model through an API, you need an LLM engineer. If your team trains, fine-tunes, or serves weights it owns, you need a machine learning engineer. Ownership decides the seat, not seniority.
Most teams that describe the problem to us as “we need an ML engineer” actually need the first one. They’ve built a feature on a hosted model, and what’s blocking them is retrieval quality, evaluation, and cost, none of which improves by hiring someone who can pretrain a transformer from scratch.
How fast can KORE1 fill an AI role in the Bay Area?
KORE1 averages 17 days to first qualified submit, and LLM or applied AI contract searches in San Francisco land close to that number. Research-track and cleared-adjacent hires typically run longer.
What actually stalls a Bay Area search isn’t sourcing. It’s an offer that arrives a week after the candidate’s other conversation already closed, after a hiring manager who was genuinely excited about the candidate on Tuesday spends the rest of the week routing the number through legal, finance, and a VP who wants one more interview before anyone signs anything. Speed on your side matters as much as speed on ours, and we say that to every client on the first call, not after the search has stalled.
Why would a strong candidate pick a startup over OpenAI or Anthropic?
Scope, mostly. A senior engineer at a frontier lab often owns one narrow slice of one system. A smaller company can offer ownership of the whole pipeline, a title bump, and equity priced early rather than after a valuation run-up.
That case has to be made explicitly, in the offer conversation, not implied by the job posting. Candidates who take it usually say the same thing afterward, and it’s rarely about the number. What kept them, in their own words, was ownership of a real decision instead of someone else’s roadmap. Money brought them to the table. Ownership is what kept them there.
Do AI engineers have to work onsite in San Francisco?
No for most of it. LLM, ML, and platform roles run fine remote or hybrid, and a meaningful share of what we place in the Bay Area lands that way. The exceptions tend to be Peninsula companies with a firm in-office culture and roles tied to physical infrastructure.
Proximity still buys something a job posting never captures. An engineer who can walk over and watch a failure case land with the product team in real time converges faster than one reading eval summaries from two time zones away. We ask about this on the intake call, not after we’ve built a pipeline that won’t accept the terms.
What should we ask in a first technical screen?
Ask how they knew the system was working. An engineer who has shipped AI describes an evaluation set and a score that moved. One who hasn’t describes a benchmark name or a general impression, and that gap shows up in under a minute.
Follow it with cost per request and what they removed to bring it down. Four questions, under ten minutes, and they separate real candidates from resume-polish better than any take-home we’ve seen used in this market.
We’ve been trying to fill this AI role ourselves for months. What’s going wrong?
Usually one of three things. The req describes a research seat when the work is applied, the filter demands years of production LLM experience that barely exist yet in this field, or the comp offer never accounted for the equity a candidate is walking away from.
The equity gap is the one we see most in San Francisco specifically, and it’s rarely fixed by adding more cash. Send us the job description and the actual offer structure together. The mismatch between those two documents is usually the whole answer.
Most AI searches in San Francisco fail on speed, not on the market.
Send the job description and the actual comp structure. We’ll tell you honestly whether you can win the offer, and come back with a date you can plan around.
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