Recruits Lab places machine learning engineers, research scientists, and ML infrastructure talent at companies running real ML in production. We screen on shipped work, not resume keywords.
Machine learning hiring is harder than it looks. The keyword 'machine learning' appears on millions of resumes. The number of engineers who have actually shipped a production ML system, owned the data pipeline, and held the pager when it broke is a fraction of that.
Recruits Lab is a specialized machine learning recruiting firm placing ML engineers, ML research scientists, ML infrastructure engineers, and ML platform leaders at companies running real ML at meaningful scale. We work with ML-native startups, enterprise ML organizations, and the embedded ML teams inside life sciences, fintech, and ad tech.
Our recruiters know the difference between a model developer and a production ML engineer. They can tell whether 'I built a recommendation system' meant owning a Kafka-fed feature store with online inference or training a notebook model that never shipped. That depth saves you weeks of bad slates.
We support hiring across the full ML stack: feature engineering, training infrastructure, model serving, observability, MLOps, and applied research. Specializations include recommender systems, ranking, search, computer vision, NLP, time series, and increasingly LLM-adjacent workloads.
Whether you are hiring your first ML engineer or building out a 20-person ML platform team, this practice area has been built for it.
Why this market is hard to recruit for. And how we solve each one.
Most ML resumes describe Jupyter projects. We screen on production ownership.
The most valuable ML engineers can do both data engineering and modeling. They are rare. We map them.
Hiring a research scientist into an applied role fails 70 percent of the time. We will tell you which you actually need.
ML comp has moved with AI comp. Stale offer bands lose candidates fast.
Senior ML engineers get aggressive retention. We close on equity and impact.
Ranking, NLP, CV, and time series have different talent pools. We do not blur them.
The repeatable system behind our 14-day average hire time.
Map the modeling stack, data scale, serving requirements, and domain before recruiting.
Outreach to engineers at ML-mature companies in the same domain.
Verify shipped systems through portfolio, public talks, and structured technical screen.
5-7 candidates per slate with technical, motivation, and comp briefs.
Manage counter-offers and equity translation.
2026 U.S. base salary ranges for machine learning roles at venture-backed companies. Sign-on, equity, and bonus excluded.
| Role | Base Salary Range |
|---|---|
| Senior ML Engineer | $195K–$265K |
| Staff ML Engineer | $270K–$385K |
| Principal ML Engineer | $385K–$540K |
| ML Research Scientist | $235K–$410K |
| ML Infrastructure Engineer | $210K–$320K |
| Director of ML | $310K–$465K |
| VP of ML / Engineering | $370K–$540K |
Quick answers to the questions founders and hiring leaders ask most.
The best machine learning recruiters specialize by domain (ranking, NLP, CV) and screen on shipped production work rather than resume keywords. Recruits Lab places ML engineers, research scientists, and ML platform leaders with a 14-day average hire time.
Senior ML engineers in the U.S. cost $195K-$265K base in 2026. Staff engineers cost $270K-$385K. Equity at venture-backed companies typically adds $300K-$1.5M over four years.
AI engineer is a newer title typically applied to roles building on top of foundation models (LLMs, multimodal). ML engineer is the longer-standing role covering full ML lifecycle including training models from scratch. The skill sets overlap, but ML engineers tend to have deeper infrastructure and modeling fundamentals.
Yes. We place ML research scientists at AI startups, applied research groups, and life sciences companies.
Industry average is 60-90 days. Recruits Lab averages 14 days from kickoff.
Needed a Principal ML Engineer to lead ranking and personalization. Two retained search firms had spent 5 months without an offer.
Recruits Lab mapped 60 ranking engineers at marketplaces and ad-tech companies, ran warm outreach, and presented 4 calibrated candidates in 11 days.
Offer signed in 18 days. Ranking lift of 12 percent in candidate's first quarter. Now Director of ML.
"They knew which Lyft and Pinterest ranking engineers were open. We hired one of them."
"Best ML hiring partner we have used. The screening was real."
Yes, both. We will calibrate which one fits your role.
Ranking, recommendations, search, NLP, computer vision, time series, RL, and increasingly LLM-adjacent applied work.
Yes. ML platform and MLOps is a growing practice area.
Yes. We have placed engineers from Google, Meta, Apple, Netflix, and Amazon.
Roughly half remote, half hybrid in 2026.
Subscription from $7,500/month or 20 percent flat contingency with a 90-day guarantee.
Kickoff within 48 hours.
Yes for Director and VP level.
Compensation data, hiring playbooks, case studies, and related recruiting specialties.
Four ways to engage. Pick whichever matches where you are.
30-minute strategy review with a senior recruiter. Free, no commitment.
Get startedTell us about the role. We respond with a calibration call within 24 hours.
Get startedSend a job description. We confirm fit and quote a subscription or contingency engagement.
Get startedSenior candidates: get represented to our active hiring pipeline.
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Hand-picked salary guides, market reports, and recruiter pages related to this topic. Authored by Darren Nelson and the Recruits Lab team.
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