Hire machine learning engineers who ship models

A machine learning engineer takes a model from a promising notebook to a system other people can rely on. They train and evaluate, watch data drift, and serve a result inside a product, a decision, or a nightly job.

The role usually covers:

• Training. Reproducible runs, with the data version and the code version written down.
• Evaluation. A metric the business recognises, not only a leaderboard number.
• Serving. A job or an API, with a way to roll back.

If the work is a language-model feature rather than a classical model, look at AI and LLM engineers. If the work is still the data underneath, start with Python data talent.

You probably need this hire when:

• A data scientist has a model the product team wants to ship, and nobody owns the path to production.
• A model is live and nobody notices when the inputs change.
• You are hiring the first person who will both train and operate the model.
• Batch scoring or a realtime feature is on the roadmap and the platform is not.

Evaluation before launch. They can say what "good" meant, what they compared against, and what would make them turn the model off.

Production habits. Logs, a rollback, and a training set they can rebuild.

Respect for the data. They will not ship a model on a table they do not understand. Many of the best ones have been close to the pipeline.

What to ask

• Tell me about a model you shipped. How did you know it was safe to turn on?
• What did you monitor in the first month, and what surprised you?
• When would you refuse to put a model in the product?

carbon.talent is a data and AI talent platform. Post a role, see who applied, and hire.

1. Post a role. Title, stack, seniority, and whether you want permanent, contract, or fractional. Set remote or on-site, the country, and the overlap hours.
2. See who applied. Open each profile. Review a real-world data experience test, a working-style profile, and a video interview when they are on the profile.
3. Hire. Interview, agree the terms with the person, and hire on the platform. ID checks are on Team and Scale.

Already found someone? Test a candidate you found, then decide.

Companies post this role from anywhere. You choose where the person is based and which hours they overlap with your team.

Remote, hybrid, and on-site use the same flow. Location is a setting on the role.