ML Challenge Task Auditor Mercor · Remote — United States $70-90/hr →

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. #J-18808-Ljbffr Dorado