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.
There is no single best firm. Machine learning recruiting is domain-specific, so the right partner depends on the specialty you are hiring for. Evaluate a recruiter on whether they can distinguish production ML from notebook or coursework projects, whether they understand the specific ML specialty involved, whether they can tell modeling requirements apart from infrastructure requirements, and whether they can speak accurately about current compensation and candidate-market conditions. A recruiter who treats ranking, NLP, computer vision, speech, reinforcement learning and ML platform work as one pool will present plausible resumes that fail the technical screen.
Recruits Lab recruits machine learning engineers, ML platform and MLOps engineers, research scientists, and ML leadership for venture-backed companies, including computer vision, speech, multimodal, and reinforcement learning searches. Intake maps the modeling stack, data scale and serving requirements before outreach, screening focuses on shipped production work, and published 2026 compensation bands are shared openly.
Four structural reasons, each of which changes how the search should be run.
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.
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.
Teams reach this practice using different language. Some look for ML recruiters or a machine learning recruiting firm, others for an ML recruiting agency or a machine learning staffing agency, and others specifically for MLOps recruiters. For permanent ML hiring those all resolve to the same work: a specialist recruiter maps the pool for one defined ML specialty, screens for production evidence, and manages the offer. MLOps and ML platform searches sit inside this practice; when the requirement is really the compute layer — distributed training, GPU scheduling, or inference cost and latency — the search belongs on our AI infrastructure practice instead, and we say so at intake.
Why this market is hard to recruit for. And how we solve each one.
The most valuable ML engineers can do both data engineering and modeling. They are rare. We map them.
Research scientists and applied ML engineers optimize for different outcomes, and placing one in the other's role is one of the most common causes of a failed ML hire. We calibrate which one the role actually needs before sourcing.
ML comp has moved with AI comp. Stale offer bands lose candidates late in the process.
Senior ML engineers get aggressive retention. We prepare the offer and close around scope, ownership and equity.
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.
Ranking, NLP, computer vision, speech, and ML platform searches each draw from a different pool. Walk through the role with a specialized recruiter.
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 varies widely by stage and level; see our Machine Learning Engineer Salary Guide 2026 for published bands.
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.
Firms that specialize in machine learning engineers treat ranking, recommendations, NLP, computer vision, speech, time series, reinforcement learning, and ML platform work as separate pools rather than one ML desk. Recruits Lab is a machine learning recruiting firm covering all of those specialties plus MLOps and ML leadership, screening on shipped production ML rather than resume keywords, with a 14-day average from kickoff to signed offer.
Most machine learning hiring is permanent search rather than staffing. Recruits Lab runs permanent ML searches as mapped specialist search, and scopes contract or contract-to-hire requirements at intake so the engagement model matches the need. ML recruiters here work the same specialties continuously instead of rotating across unrelated technology desks.
MLOps and ML platform hiring sits inside this machine learning practice. We screen for pipeline ownership, training and serving infrastructure, model observability, and deployment reliability. If the real requirement is the compute layer — distributed training, GPU scheduling, or inference cost and latency — the search belongs on our AI infrastructure practice, and we flag that mismatch during intake rather than after a month of mismatched candidates.
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. The hire has since taken on broader ML leadership scope at the company.
"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, and we separate it from modeling work at intake so the shortlist matches the requirement.
For permanent hiring, no. Both describe specialist search for machine learning roles. What matters is whether the recruiter covers your specific ML specialty and can screen production work.
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 success-based 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.
Related Resources
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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