Responsibilities
• Design multi-step ML tasks based on real research ideas (e.g., modifying RL reward functions)
• Implement changes, run training experiments, and analyze results to define correct solutions
• Build tasks around reinforcement learning concepts such as reward functions and training behavior
• Evaluate how frontier models handle your tasks and document where they fall short
• Collaborate with researchers and fellow experts to ensure tasks are consistent and rigorous

Requirements
• MSc or PhD in machine learning, computer science, or another STEM field, or equivalent practical experience in a research-heavy domain
• 1+ years of experience in a research or research-engineering role
• Hands-on experience training and evaluating ML models end-to-end (experiment setup, execution, and analysis)
• Strong familiarity with large language models and their evaluation techniques
• Working proficiency in Python and Git, comfortable with scripting and notebook environments
• Perfectionist mindset with high attention to detail, creativity, strong written communication, and ability to work independently on ambiguous problems
• Ability to engage reliably for ~35 hours per week

Preferred
• Basic understanding of reinforcement learning (reward functions, policy training)
• Past experience in AI training, model evaluation, or benchmark/task authoring

How to Apply
Click "Apply" to be taken to the Mercor website. This is not a specific job posting but an application to join their talent network. Complete your profile by uploading your resume and confirming your work location. Once verified, you will be matched to opportunities as they arise. Please note that this role cannot support H1B or STEM OPT candidates. Applying through our link supports WFH Bulletin as a referral partner, but you are welcome to apply directly if you prefer.