Member of Technical Staff - Research Engineer, Post-training

Preference Model is a superintelligence data research company building reinforcement learning environments for training capable, better-aligned AI systems. The Research Engineer will advance post-training for large language models by implementing novel approaches, building and optimizing RL infrastructure, and shaping research directions.

Responsibilities:

• Train and evaluate models on our proprietary RL environments to validate data quality, surface gaps in task coverage, and close the feedback loop between environment design and model capability
• Architect and optimize our RL training infrastructure, from training abstractions to distributed experiment management, using frameworks like Verl, OpenRLHF, or similar. Help scale our systems to handle increasingly complex research workflows
• Design, implement, and test training environments, evaluations, and methodologies for RL agents
• Profile and optimize training runs end-to-end, from data loading through reward computation, to maximize experiment throughput and shorten the research iteration cycle