Machine Learning Engineer
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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.
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 $150+/hr depending on task difficulty and specialization. Many contributors add $100k–$200k a year; some make it their full-time income.
About DataAnnotation
DataAnnotation is where 1M+ experts train the world’s leading AI models. Hundreds of millions paid to contributors to date, and the average contributor stays 5+ years. Flexible, remote, and always project-available.
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