01
Machine Learning Engineers
Engineers who design, train and productionise models, with strong software foundations and a working understanding of data pipelines.
AI & ML Engineering
Machine learning recruitmentfor teams that ship models.
Lio places machine learning engineers who take models from prototype to production, for companies building products on top of ML. We recruit for the specialist roles that decide whether a model performs reliably in the real world, and we run each search as a personal, senior-led process.
Who we recruit
The engineers we recruit
02
Applied Scientists
Scientists who turn new modelling approaches into product features, measuring impact on real user and business outcomes.
03
Model Deployment Engineers
Engineers responsible for serving models reliably at scale, with monitoring, latency and cost in mind.
04
ML-focused Data Scientists
Specialists who design experiments, evaluate models and feed what they learn back into product decisions.
05
Computer Vision and Speech Engineers
Engineers building perception and audio models for products across industries, from data collection to deployment.
06
Staff and Principal ML Engineers
Technical leaders who set architecture for ML systems and mentor the engineers building them.
Our perspective
What separates a strong ML hire
Strong machine learning engineers are judged on what they have shipped, not on the frameworks they list. The best candidates can explain why a model failed, how they measured the improvement and what they changed in the data or the system to fix it. We test for that depth in every conversation, so shortlists are built on evidence.
Demand has moved from research prototypes to models running in production. Hiring managers need engineers who understand evaluation, data quality, deployment and cost as well as modelling. We help teams define that balance before the search begins, so interviews test for the work that matters.
We work with companies across the US.
Challenges
Where ML hiring usually goes wrong
- 01
Confusing research with engineering
Many candidates describe themselves as ML engineers but have spent their careers on research. We clarify the day-to-day work of each role so both sides know what they are committing to.
- 02
Hiring for production experience
Experience running models in production is scarce and often under-described on CVs. We look for specific evidence of ownership: systems built, incidents handled and results measured.
- 03
Making the offer competitive
Strong ML engineers usually have several options. We help companies assess their offer against the market and understand what drives each candidate’s decision.
Related specialisms
Explore more of our search areas.
Get in touch
Exceptional teams start with the right people.
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