We are looking for ML Challenge Task Auditor candidates for a project delivered through the hiring partner.
About the role
Evaluate the quality, correctness, and methodological rigor of applied machine-learning tasks used to train and evaluate a frontier AI lab's models. You'll assess experiment design, model-selection reasoning, and evaluation methodology — and provide clear, rubric-based written feedback.
Basic Qualifications
- 3+ years hands-on applied/experimental ML (experiment design, model selection, hyperparameter tuning, evaluation methodology)
- 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 Qualifications
- Competition / benchmark experience (e.g., Kaggle)
- Graduate research or publication record in applied ML
- Prior task-grading or peer-review experience
- Note
- this role evaluates applied/experimental ML rigor — it is not an LLM-application-building or MLOps role.
Who you work with
Project and contracting process: the hiring partner. Applications continue on the provider's website.
Pay and hours
This role pays $70–$90/hr, for 40 h/week. The work is remote. That is about 60% above the $50 an hour median for AI & data roles open now.
Who can apply from where
Open to candidates in United States. With a profile, Tier1 checks your country and languages against this role and every other before you apply.