Auditors check applied machine-learning tasks for experiment design, metric gaming and evaluation methodology, then write rubric-based feedback. It suits applied ML practitioners with several years of experimental work who can reproduce results and critique claims.
Sobre a vaga
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.
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