Senior ML Engineer - Reinforcement Learning Environments

preference model • United States

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AI Summary

Design and build RL environments and reward functions for frontier models on ML research and engineering tasks. Collaborate with others to brainstorm and create new ideas and tools. Expertise in DL/ML research area with publications or public code.

Key Highlights

Design and build RL environments and reward functions

Collaborate with others to brainstorm and create new ideas and tools

Expertise in DL/ML research area with publications or public code

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Key Responsibilities

Design and build RL environments and reward functions

Conduct experiments and evaluations

Deliver work into production training runs

Technical Skills Required

Python PyTorch JAX CUDA Triton Pallas

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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

Transformer internals

Training/inference of modern LLMs

Inference libraries (vLLM, SGLang, etc)

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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