About this opportunity
Confidential lists this Machine Learning Task Auditor opportunity in Elgin, Texas. Review the employer’s description below for duties, qualifications and application requirements.
Job description
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.
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.