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Who this role suits
Analysts and data scientists who want flexible remote work in statistics, modelling and evaluation.
Full job description
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.
Last checked open on Oct 28, 2026. Job description provided by Mercor.
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