Machine Learning Evaluator - Fully Remote | Upto $90/hr

mercor; United States; Remote; USD 96,000 - 124,000; Part time

Benefits offered by this job

Remote work

Job summary

Mercor is seeking an ML Challenge Task Auditor to evaluate applied machine-learning tasks, assess experiment design, and provide rubric-based feedback to improve model performance. You will review results, reproduce findings, and work independently on a remote, contract basis.

The role requires 3+ years of hands-on ML experience, strong data-quality discipline, and proficiency with PyTorch, TensorFlow, scikit-learn, and XGBoost. Remote work options are available for this contract position.

Qualifications

• 3+ years hands-on applied/experimental ML experience.
• Strong data-quality rigor: leakage detection, metric gaming, and train/test hygiene.
• Proficiency with standard ML frameworks (PyTorch, TensorFlow, scikit-learn, XGBoost).
• Ability to critique ML claims against evidence and reproduce results.

Responsibilities

• Evaluate the quality, correctness, and methodological rigor of applied ML tasks for training and evaluating models.
• Assess experiment design, model-selection reasoning, and evaluation methodology.
• Provide 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.

Skills

Applied ML experience

Data quality rigor

Reproducible results

Critique ML claims

Model evaluation

Tools

PyTorch

TensorFlow

scikit-learn

XGBoost

Job description

About the job

Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark , General Catalyst , Peter Thiel , Adam D'Angelo , Larry Summers , and Jack Dorsey .

Position

ML Challenge Task Auditor

Type

Contract

Compensation

$70–$90/hour

Location

Remote

Role Responsibilities

• 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 Must-Have

• 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.

Preferred

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