Hire machine learning engineers
Machine learning engineering is the discipline that predates the current wave and still does the heaviest lifting: data pipelines, training, serving, monitoring, and the drift that shows up three months after launch. If you own models rather than rent them, this is the hire.
Free to search, every filter open · opens filtered to ML/AI Engineering.
Scored profiles matching machine learning engineers
How to tell a real one from a résumé that says the words
- Has owned a model in production past its launch — retraining, drift, the incident where predictions quietly degraded.
- Treats data as the product. Most model problems are labelled-data problems wearing a disguise.
- Can size infrastructure honestly: what needs a GPU, what does not, and what the monthly bill looks like.
- Comfortable with both worlds — classical ML where it wins, LLMs where they win. Tool loyalty is a warning sign.
How to hire machine learning engineers with this database
- Filter to ML/AI Engineering, then add the infrastructure or model-customization capability depending on whether you need serving or training.
- Use years of experience here more than on other pages: production ML has a real learning curve and the field is old enough to have seniors.
- Interview on one deployed model: how it was evaluated offline, how that compared to online, and what they did about the difference.
Every filter is free and each search shows the top 10 matches with the true total. The full ranked list is a single one-time payment — see pricing.
Questions about hiring machine learning engineers
What is the difference between an ML engineer and an AI engineer?
An ML engineer builds and operates models — training, serving, monitoring. An AI engineer builds products on models that already exist. Different day-to-day work, different interviews, frequently confused in job specs.
Do I need an ML engineer if I am only using APIs?
Usually not at first. API-based products are engineering problems, and an AI or LLM engineer is a closer fit. You need ML engineering when you start fine-tuning, self-hosting, or building models on proprietary data.
What should I look for in an ML engineer's profile?
Evidence of the full loop: a model that reached production, an evaluation that caught something, and operational work after launch. Kaggle results and course certificates show aptitude, not the loop.
Hiring for a different AI role?
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Everyone in here is scored 0–100 on what they have demonstrably shipped, with the reason behind every number.
Search the database →Scores reflect public professional signals only, and anyone can ask to be removed. Hiring decisions stay yours — this is a starting point, not a verdict on anyone's ability. Being hired for AI work yourself? Score your own profile.