ML Challenge Task Auditor
- Compenso: da 70 USD a 90 USD / ora
- Contratto: Freelance
- Chi può candidarsi: Stati Uniti
- Pubblicata il 28 ago 2026
In breve
This remote role involves evaluating the quality, correctness, and methodological rigor of applied machine learning tasks to provide clear written feedback. Candidates need at least three years of hands-on applied machine learning experience and proficiency with standard frameworks like PyTorch and TensorFlow.
Descrizione
Descrizione in inglese, così come pubblicata da Mercor.
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.