Unknown Company

ML Challenge Task Auditor (Train AI Models Part Time!)

remote, united states • Posted Today
Onsite Full Time General
hackajob is collaborating with Mercor to connect them with exceptional professionals for this role.
Evaluate the quality, correctness, and methodological rigor of applied machine-learning tasks used to train and evaluate a frontier AI lab's models. You'll assess experiment design, model-selection reasoning, and evaluation methodology — and provide clear, rubric-based written feedback.
Basic Qualifications
- 3+ years hands-on applied/experimental ML (experiment design, model selection, hyperparameter tuning, evaluation methodology)
- Strong grasp of data-quality rigor: leakage detection, metric gaming, and train/test/CV hygiene
- Proficiency with standard ML frameworks (PyTorch, TensorFlow, scikit-learn, XGBoost)
- Ability to critique ML claims against evidence and reproduce results
Preferred Qualifications
- Competition / benchmark experience (e.g., Kaggle)
- Graduate research or publication record in applied ML
- Prior task-grading or peer-review experience
Note: this role evaluates applied/experimental ML rigor — it is not an LLM-application-building or MLOps role.

ML Challenge Task Auditor (Train AI Models Part Time!) in remote at Unknown Company

This position is listed as full time and onsite.

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