Pay: up to $40 – $150+ /hour (depends on task difficulty and specialization)

Location: Remote · Type: Independent contractor · Hours: Flexible hours · Payouts: Weekly

Overview

Models are surprisingly bad at reasoning about themselves: training dynamics, evaluation design, data pipelines, and deployment trade-offs. Plausible-sounding ML advice is often subtly wrong.

As a Machine Learning Engineer you'll stress-test how models reason about ML systems and write the answers a strong practitioner would give, shaping how the next generation handles your field.

What you’ll actually do

Write prompts that probe how models reason about training, evaluation, debugging, and productionizing ML systems.
Review AI output for subtle errors: leaky evaluations, wrong loss formulations, and misdiagnosed training failures.
Write the correct solution when the model falls short, grounded in real practitioner experience.

Roles this fits

Common backgrounds: ML Engineer, MLOps Engineer, Applied Scientist.

What we look for

Hands-on experience training, evaluating, or deploying models professionally or in serious personal work.
Comfort with the modern ML stack; most tasks assume Python and PyTorch or JAX.
Clear written English: your explanations are the training signal.
No degree required. We care about what you can do, not where you learned it.

Compensation

Up to $40 – $150+/hr depending on task difficulty and specialization. Many contributors add $10k–$100k+ a year; some make it their full-time income.

About DataAnnotation

DataAnnotation is where 100k+ experts train the world’s leading AI models. $150M+ paid to contributors to date, and the average contributor stays 5+ years. Flexible, remote, and always project-available.

How it works

Apply, pass the qualification assessment, then pick up paid projects whenever you want and get paid weekly.

Machine Learning Engineer

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