Data & Machine Learning AI Training Jobs

Data and machine learning AI training jobs are the meta-layer of this market: labs hiring ML-literate people to design the evaluations, curate the datasets and audit the reward signals that everyone else’s work feeds into. If the other categories produce training data, this one decides what good data looks like.

The live listings below come from Mercor, Outlier, Alignerr, micro1, DataAnnotation and Terac, refreshed daily, with rates as published.

88 open data & machine learning listings live right now.

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Language & audio197Software engineering150Science & research150Finance & accounting117Law105Business operations98Data & machine learning88Medicine & healthcare83Arts & design80Cybersecurity72Engineering (other)51Sales & marketing38
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Commitment
Contract / hourly848Part time51Full time29Task based25
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Remote936Hybrid14On site3
Marketplace
Alignerr486Mercor418micro1364DataAnnotation19Outlier15Terac11
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What the work looks like

Typical projects: writing evaluation rubrics and grading guidelines, building benchmark tasks, reviewing preference data for label quality, red-teaming models for failure modes, and analyzing where a fine-tune went wrong. Some roles are hands-on with Python and data tooling; others are pure judgment work on other contributors’ output.

This is the one category where ML knowledge is the product. Everywhere else the labs want domain experts without AI backgrounds; here they want people who know what a held-out set is and why a reward model drifts. Data scientists, ML engineers and quantitative analysts fit the bill.

Where these listings come from

Six marketplaces publish this work openly. Every listing above links to the one that posted it, and each has its own page here covering pay, screening and who gets hired.

MercorOutlierDataAnnotationAlignerrmicro1Terac

The screen is where these roles are won

Every marketplace above screens with an interview or assessment in your own domain before assigning paid work. Run that interview with Skillora first and get feedback on how you explained your reasoning, not just on what you said.

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