Machine Learning Engineer (AI Training)
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 doWrite 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 $150+/hr depending on task difficulty and specialization. Many contributors add $100k–$200k a year; some make it their full-time income.
- About Data
- AnnotationDataAnnotation 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.
- Apply now
- Hourly rate up to $150+ / hour
- Commitment Flexible hours
- Type Independent contractor
- Payouts WeeklyApply now