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Research Scientist - AI Safety

Faculty· Research Science
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Hybrid (London)Full-timePosted 2mo ago

About the role

Research Scientist - AI Safety at Faculty conducts original technical research in AI safety to build the foundation for safe, trustworthy AI deployment. Designs and executes experiments, studies model behavior, develops robust tooling, and communicates findings to external partners and safety institutes.

Responsibilities

  • Executing technical research in AI safety by designing and running experiments to study and steer model behaviour.
  • Applying white-box and black-box methodologies, focusing on interpretability, steering, and behavioural studies of transformer-based LLMs.
  • Developing high-quality, modular, and reproducible research code in Python using deep learning frameworks like PyTorch.
  • Building and maintaining research infrastructure and technical tooling to accelerate scientific discovery.
  • Documenting and communicating experimental findings through high-quality academic papers, technical reports, and presentations.
  • Collaborating with senior scientists to identify literature gaps, refine hypotheses, and identify research scope.
  • Translating complex technical insights for diverse audiences, including external frontier labs, government bodies, and safety institutes.

Qualifications

  • Solid background in machine learning fundamentals and transformer architectures (specifically LLMs).
  • A practical understanding of white-box and black-box interpretability techniques, such as steering vectors or mechanistic interpretability.
  • Demonstrated capability to take structured technical problems and formulate hypotheses.
  • Strong programming skills in Python and deep learning frameworks like PyTorch, with a track record of writing clean, modular research code, using Git for version control.
  • Excellent communication, enabling you to translate complex technical concepts and experimental results into clear reports and presentations.
  • Curiosity and analytical rigour, combined with exposure to core AI safety areas such as robustness, evaluations, or uncertainty calibration.
Last updated Oct 21, 2026. Interested? Apply on the company site.
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