Research Scientist - Environments, Data and Post-Training

Tech Stack

Responsibilities

• Implement novel post-training methods that improve model reasoning, tool use, and agentic behavior.
• Design and run experiments across datasets, reward functions, environments, and optimization strategies, including methods such as GRPO and DAPO.
• Build reinforcement learning with verifiable rewards (RLVR) and other post-training pipelines at scale.
• Create methods for measuring data quality, usability, and causal impact on model performance.
• Build scalable pipelines for data generation, filtering, augmentation, and selection.

Benefits

• Equity
• Gym Membership
• Health Insurance
• Remote Work

Culture

Requirements