Machine Learning Expert Contributor

Create advanced training and evaluation tasks spanning supervised learning, deep learning, model behavior and experimentation.

Key responsibilities

• Author demonstrations and preference pairs.
• Design failure cases and benchmark prompts.
• Evaluate technical depth and reproducibility.
• Recommend taxonomy improvements.

What we look for

• Applied ML, AI research or advanced engineering experience.

How success is measured

Technical depth, acceptance rate, benchmark usefulness.

How this works on Jwuma

Candidate profile → skills evidence → domain assessment → calibration task → qualification → project matching → production → peer review → expert QA → performance feedback and progression. Corpshore AI service alignment Applicable across annotation and labeling, RLHF and preference data, model evaluation and red-teaming, multimodal datasets and specialized AI data operations, depending on project scope.

Common questions