ML Engineer
Recruiting ML engineers who ship models to production. AI-assisted sourcing, behavioural assessment and a human approving every candidate, in San Francisco, New York, Austin and London.
See ML Engineer →By Carla Burger, founder of PeopleNotResumes. Updated September 2026.
'AI engineer' covers several different jobs. An ML engineer trains, deploys and maintains models and the infrastructure around them. An applied AI or LLM engineer builds products on top of foundation models. A research engineer works on new methods, usually at labs or research-heavy teams. A founding engineer at an AI startup does a bit of everything. Hiring the wrong profile is the most common and most expensive mistake.
Recruiting ML engineers who ship models to production. AI-assisted sourcing, behavioural assessment and a human approving every candidate, in San Francisco, New York, Austin and London.
See ML Engineer →Recruiting applied AI and LLM engineers who build reliable products on foundation models: retrieval, agents, evaluation and cost control. Human-approved shortlists in SF, NYC, Austin and London.
See Applied AI / LLM Engineer →Recruiting founding engineers for AI startups: builders who ship fast, own the whole stack and make good calls with little structure. Searches in San Francisco, New York, Austin and London.
See Founding Engineer →Recruiting account executives and solutions engineers who can sell AI and agentic products to technical and enterprise buyers. Human-approved searches in SF, NYC, Austin and London.
See AI Account Executive & Solutions Engineer →Titles in AI are inconsistent and CVs are full of the same model names. Ask candidates to walk through something they shipped: how they chose an evaluation method, what broke in production, how they balanced quality against cost and latency. Structured interviews that use the same questions and scoring for every candidate make these comparisons fair. Behavioural assessment adds signal on ownership, judgment and how someone works under ambiguity.
It depends on seniority, location and how quickly you decide. Senior searches commonly run from several weeks to a few months. In London, notice periods of one to three months add time before a start date. The biggest controllable factor is your own process: slow feedback and long loops lose candidates to faster competitors.
Describe the real problem, the current stack, what the person will own in their first 90 days and who they will work with. Separate genuine requirements from nice-to-haves. Candidates skip job descriptions that list every AI framework and say nothing about the work.
Yes, with safeguards. New York City's Local Law 144 requires bias audits and candidate notice for automated employment decision tools. California regulations on automated-decision systems in employment took effect in October 2025. The UK expects meaningful human involvement in significant automated decisions, and the EU AI Act treats recruitment AI as high-risk. The safest pattern is AI for sourcing and organisation, and a human for every candidate decision. Texas: see /ai-recruiters/austin.
We turn the hiring manager's real bar into a behavioural scorecard: what great looks like in the first 90 days, which trade-offs matter, and which signals on a CV are noise.
AI tooling maps the market, including engineers who are not on job boards and people whose titles do not match what they actually build.
Every candidate who is advanced or rejected is reviewed by a person. Shortlists come with written reasoning you can audit, not a black-box score.
Structured interviews and behavioural assessment test judgment, ownership and how someone ships under ambiguity, which is what separates strong AI engineers from strong interviewers.
An ML engineer usually trains and deploys models and owns ML infrastructure. An AI engineer, often called an applied AI or LLM engineer, usually builds products on top of existing foundation models. The skills overlap, but the day-to-day work differs.
A specialist recruiter helps most when the role is senior, the market is competitive or your team lacks time to source. Look for one who can assess AI depth, explains their process and keeps a human accountable for every candidate decision.
San Francisco, New York, Austin and London, plus remote and hybrid teams across the US and UK.
Tell us the role and where the pipeline is stuck. You'll hear back within one business day with a scope, a timeline, and a fee.
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