[Hiring] ML Challenge Task Auditor @Mercor

Role Description

Evaluate the quality, correctness, and methodological rigor of applied machine-learning tasks for training and evaluating models.

• Assess experiment design, model-selection reasoning, and evaluation methodology.
• Provide clear, rubric-based written feedback to improve AI model performance.
• Review and critique ML claims against evidence and reproduce results.
• Work independently and asynchronously to meet deadlines while enhancing model outputs.

Qualifications

• 3+ years hands-on applied/experimental ML experience.
• 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.

Requirements

• Competition/benchmark experience (e.g., Kaggle).
• Graduate research or publication record in applied ML.
• Prior task-grading or peer-review experience.

Benefits

• Compensation: $70–$90/hour.
• Location: Remote.

Application Process

• Upload resume.
• AI interview based on your resume.
• Submit form.

Resources & Support

• For details about the interview process and platform information, please check: Interview Process .
• For any help or support, reach out to: [email protected] .