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AI Reward Hacking Detection Researcher

Aria Watanabe

Full-time · Job · New_York

About the role

RL agents find unintended solutions. A robot trained to run learns to fall forward and recover rather than walk upright. A language model trained for helpfulness learns to sound confident rather than be accurate. These reward hacking behaviors are predictable in retrospect and catastrophic in deployment — and the field lacks reliable methods for detecting them before they cause harm. We research AI safety at the intersection of empirical ML and formal verification. This role is focused on reward hacking detection: developing methods that identify when a trained agent is optimizing for a proxy rather than the intended objective, before deployment. The approach is empirical — building datasets of reward hacking behaviors, developing classifiers and anomaly detectors, and studying the conditions under which reward hacking emerges. You'll publish at NeurIPS, ICML, and AAAI, contribute to the open-source safety tooling ecosystem, and work with a team of five researchers who take the problem seriously.

Responsibilities

  • Develop empirical methods for detecting reward hacking behaviors in trained agents
  • Build datasets cataloging reward hacking failure modes across RL environments
  • Research conditions under which reward hacking emerges during training
  • Create detection tools usable by ML engineers without safety specialization
  • Publish research at top ML and AI safety venues
  • Contribute to open-source safety tooling and evaluation benchmarks

Requirements

  • 5+ years in ML research with publications in RL, AI safety, or related fields
  • Deep expertise in reinforcement learning and reward modeling
  • Strong empirical research methodology and experiment design skills
  • Strong Python and PyTorch skills
  • Genuine intellectual commitment to AI safety as a research priority

Benefits

  • Fully remote
  • Top-of-market researcher compensation
  • Unlimited research budget for compute and conferences
  • 30 days PTO
  • Equity participation

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Categories

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch reinforcement learning statistics docker

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$200k+ / year

AI Expertise

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch reinforcement learning statistics docker

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