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.