Security/Cybersecurity Engineer for Reinforcement Learning Environments

preference model • United States

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AI Summary

We're hiring experienced Security/Cybersecurity Engineers to design and build reinforcement learning environments that teach LLMs to reason about and solve real-world cybersecurity problems.

Key Highlights

Design and build RL environments and reward functions

Teach LLMs to reason about and solve real-world cybersecurity problems

Collaborate with others to brainstorm and create new ideas and tools

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Key Responsibilities

Design and build RL environments and reward functions

Build environments covering the full vulnerability lifecycle

Construct verifiable reward signals

Technical Skills Required

Python Systems programming C C++ Rust Fuzzers Sanitizers Debuggers Disassemblers

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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

Experience building or contributing to fuzzing infrastructure

Experience with ML for code or security

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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