Data annotation job
ML Challenge Task Auditor
Mercor · Posted 2 months ago
- AI evaluation
- LLM training
About 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.
This listing is aggregated by DataAnnotationJobs.org. Applications are processed by Mercor, not by this site.
Mercor is an AI-native talent marketplace connecting vetted domain experts such as doctors, lawyers, and engineers directly with frontier AI labs for RLHF, evaluation, and specialized training data at massive scale. See all Mercor jobs.
Apply for this role
Your details first, then a short screening test. Both are read as part of your application and go to Mercor, and we will tell you when similar roles appear.