Machine Learning Task Auditor

Role Overview Help strengthen the applied machine-learning tasks used to train and evaluate advanced AI models. You will assess task quality, correctness, and methodological rigor, with particular attention to experiment design, model-selection reasoning, and evaluation methodology. This is an applied and experimental ML review role, not an LLM application development or MLOps position. Key Responsibilities Evaluate applied machine-learning tasks for quality, correctness, and methodological soundness. Review experiment design, model-selection rationale, and evaluation methodology. Provide clear, rubric-based written feedback on task quality and rigor. Assess ML claims against supporting evidence and reproduce results when needed. Qualifications At least 3 years of hands-on applied or experimental machine-learning experience, including experiment design, model selection, hyperparameter tuning, and evaluation methodology. Strong understanding of data-quality rigor, including leakage detection, metric gaming, and sound train, test, and cross-validation practices. Proficiency with standard ML frameworks, including PyTorch, TensorFlow, scikit-learn, and XGBoost. Ability to critically evaluate ML claims using evidence and reproduce results. Preferred Qualifications Competition or benchmark experience, such as Kaggle. Graduate research experience or a publication record in applied machine learning. Previous task-grading or peer-review experience. Work Terms Remote, United States. Hourly engagement. Compensation $70 to $90 per hour.