Research and Engineering Engineer

preference model • United States

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AI Summary

Design and build RL environments, develop scalable training infrastructure, and create model evaluations. Requires 4+ years of software engineering experience and expertise in at least one domain. Collaborate with other teams to drive technical strategy and build a strong engineering culture.

Key Highlights

Design and build RL environments

Develop scalable post-training systems

Create model evaluations

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Key Responsibilities

Design and build RL environments end-to-end

Build RL training infrastructure

Create model evaluations

Shape technical strategy

Drive architecture decisions

Technical Skills Required

Python Rust TypeScript Kubernetes AWS GCP

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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

ML infrastructure or RL systems experience

Simulation environments or LLM eval pipelines

Distributed systems or performance optimization

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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