Plenty of candidates can train a model. Fewer can own the data, the evaluation and the deployment when something breaks. We find the second group.
Start a searchStrong ML engineers are often hiding behind titles like data scientist, backend engineer or research engineer. We map candidates by what they have shipped: production systems, evaluation work and infrastructure they owned, then reach them with a specific reason to talk.
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.
In most teams, a data scientist focuses on analysis and modelling, while an ML engineer owns getting models into production and keeping them reliable. Many strong ML engineers started as data scientists, so we assess what someone has actually shipped rather than their title.
We use structured interviews built around real trade-offs: how a candidate chose an evaluation metric, handled a data quality problem or balanced accuracy against latency and cost. Behavioural assessment covers ownership and judgment under ambiguity. A human reviews every candidate decision.
We run ML engineering searches in San Francisco, New York, Austin and London, and for 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.
Start a search