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

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.