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ML Challenge Task Auditor

$70–$90/h

United States

Role overview

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.

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Future of work · September 2026

How big is the remote / hybrid opportunity?

Remote work is already huge — but the bigger story may be the hundreds of millions of jobs that could still move beyond geography.

Global remote and hybrid work opportunity infographic

Remote work already connects millions of people with companies they would never have been able to work for a decade ago. But compared with the size of the global labour market, it may still be early.

The interesting number is not only how many people work remotely today — it is how many roles could plausibly become remote or hybrid next.

The chart above starts with roughly 3.3 billion people employed worldwide. Around 300 million jobs are already remote or hybrid, while an additional pool of roughly 250 million roles could potentially move in that direction as technology, workflows and company culture continue to change.

Skills are becoming more portable

This matters because more work can increasingly be matched to expertise rather than location. AI training and evaluation are a good example: companies need lawyers, engineers, designers, editors, researchers, finance professionals and many other specialists to help improve AI systems.

For workers, that creates access to projects outside their local market. For companies, it expands the talent pool from one city or country to the world.

Explore remote opportunities →