Applied ML Evaluation Auditor

Obsidian is seeking an applied ML evaluator to assess the quality, correctness, and rigor of experiments used to train frontier AI models. You will review experiment design, model-selection reasoning, and evaluation methodology.

This role emphasizes data-quality hygiene, leakage detection, and reproducibility—no MLOps duties. Experience with PyTorch, TensorFlow, scikit-learn, and XGBoost is expected; prior peer review or benchmarking is a plus.