Research Engineer / Scientist, Alignment at Anthropic

Hybrid - London, UK

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You want to build and run elegant and thorough machine learning experiments to help us understand and steer the behavior of powerful AI systems. You care about making AI helpful, honest, and harmless, and are interested in the ways that this could be challenging in the context of human‑level capabilities. As a Research Engineer on Alignment Science, you’ll contribute to exploratory experimental research on AI safety with a focus on risks from powerful future systems, often collaborating with other teams including Interpretability, Fine‑Tuning, and the Frontier Red Team.

Salary

GBP 260,000 - 370,000

Requirements

Skills

  • Significant software, machine learning, or research engineering experience
  • Experience contributing to empirical AI research projects
  • Familiarity with technical AI safety research
  • Preference for fast‑moving collaborative projects
  • Ability to pick up slack beyond job description
  • Concern for AI impacts
  • Experience authoring research papers in machine learning, NLP, or AI safety
  • Experience with large language models
  • Experience with reinforcement learning
  • Experience with Kubernetes clusters and complex shared codebases

Responsibilities

  • Build and run elegant and thorough machine learning experiments to understand and steer powerful AI systems
  • Conduct exploratory experimental research on AI safety focusing on risks from powerful future systems
  • Test robustness of safety techniques by training language models to subvert them
  • Run multi‑agent reinforcement learning experiments such as AI Debate
  • Build tooling to efficiently evaluate novel LLM‑generated jailbreaks
  • Write scripts and prompts to produce evaluation questions testing models’ reasoning in safety‑relevant contexts
  • Contribute ideas, figures, and writing to research papers, blog posts, and talks
  • Run experiments that feed into key AI safety efforts at Anthropic

Technologies

PythonKubernetesReinforcement learning frameworksLarge language models (LLMs)AI Debate

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