Member of Technical Staff - Research Engineer, Post-training

The role

This role blends research and engineering to push the frontier of post-training on large language models. You will implement novel approaches and shape research directions.

What you will do

• Train and evaluate models on proprietary RL environments to validate data quality and surface gaps in task coverage.
• Architect and optimize RL training infrastructure using frameworks like Verl, OpenRLHF, or similar.
• Scale systems to handle complex research workflows.
• Design, implement, and test training environments, evaluations, and methodologies for RL agents.
• Profile and optimize training runs end-to-end to maximize experiment throughput.

Required qualifications

• Industry experience running end-to-end LLM post-training pipelines of models sizes at least 7B in size
• Proficiency in Python and PyTorch or JAX
• Experience with at least one modern RL training framework
• Experience building and operating ML infrastructure at scale

Nice to have

• Experience evaluating model outputs and building reward or evaluation signals
• Stay current on post-training research and can translate papers into running code
• Strong opinions (loosely held) about how to structure RL training code for reproducibility and fast iteration
• Can balance research exploration with engineering rigor
• Strong systems design and communication skills

Benefits

• Competitive cash and equity compensation (>90th percentile)
• Ownership and autonomy in a fast moving startup environment
• Opportunity to work with top machine learning engineers
• Health, vision, dental, benefits
• 401K match
• Lunch provided everyday onsite
• Weekly snack orders
• Visa sponsorship & relocation support available

About the company

Preference Model is a superintelligence data research company. They build RL environments for training capable, better-aligned superintelligences.