Machine Learning Engineer
About this role
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. How it works Apply Qualify Work & get paid 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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