ML Engineer - Reinforcement Learning Environments

preference model • Canada

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AI Summary

Design and build reinforcement learning environments to safely advance model capabilities. Collaborate with researchers and engineers to develop novel approaches and realize them in code. Contribute to the data layer that powers frontier LLM capability.

Key Highlights

Design and build RL environments and reward schemes

Collaborate with researchers and engineers

Contribute to the data layer that powers frontier LLM capability

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Key Responsibilities

Design and build RL environments and reward schemes

Collaborate with researchers and engineers

Contribute to the data layer that powers frontier LLM capability

Technical Skills Required

Python PyTorch JAX Systems programming

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Benefits & Perks

Competitive cash and equity compensation

Ownership and autonomy in a fast moving startup environment

Opportunity to work with top machine learning engineers

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Nice to Have

Expert knowledge in an active DL/ML research area

Research experience (PhD, MS)

Deep understanding of transformer internals

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.We’re hiring to design and build

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