Hire a Machine Learning Engineer
Everyone claims the title. The gap between training a model and running one is where the hire is won or lost.
The title stopped meaning anything around 2023
- Retained recruiters.
- LinkedIn Recruiter seats.
- Keyword-matched resumes.
- Templated InMails.
Machine learning engineer now covers everything from fine-tuning someone else's model in a notebook to owning training infrastructure at scale. Both describe themselves identically, and a CV cannot separate them.
For most startups the job is closer to engineering than research. Data pipelines that do not silently corrupt, evaluation that catches regressions before customers do, inference that stays up and within budget. The modelling is often the smallest part.
Which means the standard filters actively mislead. Papers and a PhD signal research depth, not production reliability. A long list of frameworks signals nothing at all.
How the agent runs a machine learning engineer search
It reads the production evidence
Repositories with tests and deployment code, write-ups about a model that degraded and what they did, work on evaluation harnesses. The unglamorous parts are the signal.
It separates research from shipping
Both are valuable and you probably need one specifically. The agent reads for which one a candidate actually does and says so in the write-up.
Outreach that names the problem
Strong candidates pick by the problem and the data. The message says what you are modelling, what data you have and what is broken today.
Screening on the failure they owned
Your questions go out early: a model that got worse in production, how they found out, and what they changed.
Questions.
Answered.
AI engineer usually means building product on top of foundation models. Machine learning engineer usually means owning models, data and training. If you are unsure which you need, say what the person would do in month one and we will tell you.
Only for genuine research. For production work a PhD is neither necessary nor a strong signal, and requiring it removes a lot of good candidates.
Yes. Tell us the domain, whether that is healthcare, fraud, ranking or robotics, and the agent weights for evidence of work on that kind of data.
Every candidate read says which of the two the evidence supports, so you can decide rather than discovering it in the interview.
Find your machine learning engineer.
Tell us what you are modelling and the agent will find people who have shipped it.