Member of Technical Staff - Research & Post-training

About the role

The role is a blend of research and engineering, focusing on pushing the frontier of self-directed learning in large language models.

Responsibilities

• Train and evaluate models on proprietary RL environments to validate data quality, surface gaps in task coverage, and close the feedback loop between environment design and model capability.
• Arcitect and optimize RL training infrastructure, including training abstractions and distributed experiment management, using frameworks like Verl, OpenRLHF, or similar.
• Help scale 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.

Requirements

• Experience running end-to-end LLM post-training pipelines.
• Proficiency in Python and PyTorch or JAX.
• Experience with at least one modern RL training framework.
• Experience building and operating ML infrastructure at scale.

Qualifications

• Strong opinions (loosely held) about how to structure RL training code for reproducibility and fast iteration.
• Ability to balance research exploration with engineering rigor.
• Strong systems design and communication skills.

Skills

• Evaluate model outputs and build reward or evaluation signals.
• Stay current on post-training research and translate papers into running code.

Benefits

• Competitive cash and equity compensation.
• 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.

Pay

$200K - $350K

Schedule

Flexible work schedule to accommodate the needs of the role and the team.