AI Trainer (ML Engineering)

What You'll Do6

• Design realistic machine learning engineering tasks that challenge advanced AI models.
• Write task instructions, set up reproducible environments, and define reference solutions and evaluation criteria.
• Assess model-generated code, experiments, analyses, and technical deliverables.
• Spot problems such as data leakage, faulty validation, unstable training, performance issues, or reproducibility gaps.
• Create objective standards for what counts as a correct solution.
• Review technical submissions from other AI trainers when needed.

Requirements4

• Prior paid experience training, evaluating, benchmarking, or improving AI systems.
• Strong practical proficiency with Python.
• Hands-on experience with PyTorch, JAX, TensorFlow, scikit-learn, XGBoost, LightGBM, or similar ML tools.
• Ability to provide public evidence of skill, such as GitHub repositories, Kaggle or other competition results, Hugging Face models or datasets, research publications, open-source ML contributions, or public benchmarks.

Preferred Qualifications6

• Experience creating AI-training tasks, reference answers, rubrics, graders, or benchmark problems.
• Experience reviewing other AI trainers' technical submissions.
• Kaggle Expert, Master, or Grandmaster status, or strong results in another technical competition.
• Publications or open-source contributions in machine learning.
• Prior experience with long-horizon or agentic AI evaluations.
• Availability to begin immediately.

Who Should Apply

Machine Learning Engineers who have already completed substantive paid AI-training or model-evaluation work and can show public technical evidence. You should be comfortable with Python and ML frameworks like PyTorch or TensorFlow, and able to design and judge challenging ML tasks.

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