Research Engineer - Environments, Data and Post-Training

📋 Description

• Implement novel post-training methods that improve model reasoning, tool use, and agentic behavior.
• Develop new training recipes for frontier open models.
• Design and run experiments across datasets, reward functions, environments, and optimization
• Build reinforcement learning with verifiable rewards (RLVR) and other post-training pipelines at
• Investigate model capabilities and failure modes, then develop targeted training interventions.
• Create methods for measuring data quality, usability, and causal impact on model performance.

🎯 Requirements

• Demonstrated experience training and evaluating machine learning models.
• A strong research record in post-training, reinforcement learning, language-model evaluation
• 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.
• Excitement to work in person in San Francisco, five days a week (with optional remote Saturdays)

🎁 Benefits

• Bi-annual performance bonus structure
• Generous equity grant vested over 4 years
• Up to $15k Relocation bonus
• $10K housing bonus (if you live within 0.5 miles of our office)
• $1.5K monthly stipend for meals
• Free Equinox membership