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AI Alignment Research Engineer

Aria Watanabe

Full-time · Job · New_York

About the role

Our content moderation platform uses RLHF to align model behavior with client-specific content policies. But alignment is fragile — models that behave correctly on benchmark distributions can fail catastrophically on edge cases. We need a research engineer to study why alignment breaks and how to make it more robust. You'll design experiments that stress-test alignment: adversarial probing, distribution shift evaluation, and robustness testing across client policy variations. You'll also work on practical improvements — better reward modeling, more sample-efficient RLHF, and alignment techniques that transfer across policy configurations without full retraining. This role combines rigorous research methodology with production awareness. Every finding needs to translate into a system improvement. We publish when we can, but shipping comes first.

Responsibilities

  • Design experiments to stress-test model alignment under distribution shift
  • Build adversarial probing frameworks for alignment robustness evaluation
  • Develop improved reward modeling techniques for content policy alignment
  • Research sample-efficient RLHF methods for multi-client policy transfer
  • Translate research findings into production alignment improvements

Requirements

  • PhD or equivalent experience in ML alignment or safety research
  • Strong PyTorch skills and RLHF implementation experience
  • Published work in alignment, safety, or reinforcement learning
  • Deep understanding of reward modeling and policy optimization
  • Ability to bridge research insights and production engineering

Benefits

  • Hybrid — NYC office 3 days/week
  • $200k+ base with equity
  • Dedicated research GPU budget
  • Conference travel and publication support

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Categories

AI & Machine Learning Engineers AI Product & Strategy

Skills

python pytorch reinforcement learning statistics hugging face

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Language

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Salary Range

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AI Expertise

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Skills

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