Research Scientist - Environments, Data and Post-Training

Highlights

• Develop novel post-training methods to improve model reasoning and agentic behavior.
• Build large-scale RL pipelines and environments that shape frontier model behavior.
• Design experiments on data, rewards, and optimization to improve model performance.

What they offer

• Bi-annual performance bonus structure
• Generous equity grant vested over 4 years
• Up to $15k relocation bonus
• $10K housing bonus if within 0.5 miles of office
• $1.5K monthly stipend for meals
• Free Equinox membership
• $200 monthly laundry reimbursement
• $200 monthly personal wellness reimbursement
• Health, Dental, Vision insurance

Ideal candidate

A rigorous ML researcher excited by post-training and data-centric problems who thrives in high-intensity, in-person collaboration and wants their experiments to shape frontier models and products.

Requirements

• Demonstrated experience training and evaluating machine learning models
• Strong research record in post-training, reinforcement learning, language-model evaluation, data-centric ML, or closely related field
• Ability to reason rigorously about model behavior, experimental results, and data quality
• Strong programming skills and experience implementing machine learning systems
• Knowledge of the current AI research landscape and important open problems