Research Engineer - LLM Post-Training and RL Environment Development

preference model • United States

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AI Summary

Develop and optimize RL training environments for large language models, focusing on self-directed learning and post-training. Implement novel RL methodologies and scale infrastructure for distributed experiment management. Requires strong Python, PyTorch/JAX, and RL framework expertise with a blend of research and engineering rigor.

Key Highlights

Build RL environments reflecting real-world complexity for LLM post-training

Implement and optimize RL training infrastructure using Verl/OpenRLHF

Profile and optimize end-to-end training pipelines for maximum throughput

Experience with LLM post-training pipelines and reward signal design

No PhD required; adaptability and communication skills prioritized

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Key Responsibilities

Train and evaluate models on proprietary RL environments to validate data quality and surface gaps in task coverage

Architect and optimize RL training infrastructure from training abstractions to distributed experiment management

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

Help scale systems to handle increasingly complex research workflows

Technical Skills Required

Python PyTorch JAX RL training frameworks ML infrastructure Data loading Reward computation Distributed experiment management

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Benefits & Perks

Competitive cash and equity compensation (>90th percentile)

Ownership and autonomy

Health, vision, dental benefits

401K match

Lunch provided everyday onsite

Weekly snack orders

Visa sponsorship

Relocation support available

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Nice to Have

Experience evaluating model outputs and building reward or evaluation signals

Staying current on post-training research and translating papers into running code

Strong opinions about structuring RL training code for reproducibility and fast iteration

Balancing research exploration with engineering rigor

Strong systems design and communication skills

Job Description

Preference Model is building automated ML research engineering.Existing frontier models are brittle when applied to real-world ML tasks. The present bottleneck is the lack of high-quality RL training environments. Our first step is to build RL environments that reflect real-world complexity, with diverse tasks and robust reward functions.Our founding team has previous experience on Anthropic’s data team building data infrastructure, and datasets behind Claude. We are partnering with leading AI labs to push AI closer to achieving its transformative potential.

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