Research Engineer/Scientist - Machine Learning and Reinforcement Learning

preference model • Canada

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AI Summary

Join Preference Model to push the frontier of post-training on large language models in a role that blends research and engineering. You'll implement novel approaches, design RL environments, and optimize training infrastructure. We're looking for someone with experience in LLM post-training pipelines and modern RL frameworks.

Key Highlights

Build RL environments that reflect real-world complexity

Architect and optimize RL training infrastructure

Work with a team that has experience at Anthropic's data team

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Key Responsibilities

Train and evaluate models on proprietary RL environments to validate data quality

Architect and optimize RL training infrastructure using frameworks like Verl, OpenRLHF

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 to maximize experiment throughput

Technical Skills Required

Python PyTorch JAX Reinforcement Learning LLM post-training pipelines

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Benefits & Perks

Competitive cash and equity compensation (>90th percentile)

Health, vision, dental benefits

401K match

Lunch provided everyday onsite

Weekly snack orders

Visa sponsorship

Relocation support

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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 about how to structure RL training code for reproducibility

Ability to balance 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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