[Expression of Interest] Research Engineer / Scientist, Alignment - London

[Expression of Interest] Research Engineer / Scientist, Alignment - London

Full-Time Home office (partial)
Anthropic

At a Glance

  • Tasks: Conduct machine learning experiments to enhance AI safety and alignment.
  • Company: Join Anthropic, a leading AI research company focused on safe and beneficial AI.
  • Benefits: Competitive salary, flexible hours, generous leave, and equity donation matching.
  • Other info: Dynamic team environment with opportunities for growth and innovation.
  • Why this job: Make a real impact in AI safety while collaborating with top experts.
  • Qualifications: Experience in software, ML, or research engineering; passion for AI safety.

About Anthropic

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

About the role

You want to build and run elegant and thorough machine learning experiments to help us understand and steer the behaviour 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. You could describe yourself as both a scientist and an engineer. 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 in collaboration with other teams including Interpretability, Fine‑Tuning, and the Frontier Red Team.

Research areas

  • AI Control – Creating methods to ensure advanced AI systems remain safe and harmless in unfamiliar or adversarial scenarios.
  • Alignment Stress‑testing – Creating model organisms of misalignment to improve our empirical understanding of how alignment failures might arise.

Note: Currently, the team's hub is in San Francisco, so we require all candidates to be based at least 25% in London and travel to San Francisco occasionally. Additionally, we are prioritising growing our San Francisco teams, so you may not hear back on your application to the London team unless we see an unusually strong fit. For this role, we conduct all interviews in Python.

Representative Projects

  • Testing the robustness of our safety techniques by training language models to subvert our safety techniques, and seeing how effective they are at subverting our interventions.
  • Run multi‑agent reinforcement learning experiments to test out techniques like AI Debate.
  • Build tooling to efficiently evaluate the effectiveness of novel LLM‑generated jailbreaks.
  • Write scripts and prompts to efficiently produce evaluation questions to test models’ reasoning abilities 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, such as the design and implementation of our Responsible Scaling Policy.

You may be a good fit if you:

  • Have significant software, ML, or research engineering experience
  • Have some experience contributing to empirical AI research projects
  • Have some familiarity with technical AI safety research
  • Prefer fast‑moving collaborative projects to extensive solo efforts
  • Pick up slack, even if it goes outside your job description
  • Care about the impacts of AI

Strong candidates may also:

  • Have experience authoring research papers in machine learning, NLP, or AI safety
  • Have experience with LLMs
  • Have experience with reinforcement learning
  • Have experience with Kubernetes clusters and complex shared codebases

Candidates need not have:

  • 100% of the skills needed to perform the job
  • Formal certifications or education credentials

Annual Salary £260,000 — £370,000 GBP

Logistics

  • Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
  • Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
  • Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position
  • Location‑based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
  • Visa sponsorship: We do sponsor visas. However, we aren’t able to successfully sponsor visas for every role and every candidate. If we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

Benefits

We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues.

Guidance on Candidates' AI Usage

Learn about our policy for using AI in our application process.

[Expression of Interest] Research Engineer / Scientist, Alignment - London employer: Anthropic

At Anthropic, we pride ourselves on being an exceptional employer dedicated to fostering a collaborative and innovative work culture in the heart of London. Our commitment to employee growth is reflected in our competitive compensation, generous benefits, and flexible working hours, all while contributing to meaningful research in AI safety. Join us to be part of a passionate team that values your contributions and prioritises the development of safe and beneficial AI systems.

Anthropic

Contact Details:

Anthropic Recruitment Team

StudySmarter Expert Advice🤫

We think this is how you could land [Expression of Interest] Research Engineer / Scientist, Alignment - London

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We think you need these skills to ace [Expression of Interest] Research Engineer / Scientist, Alignment - London

Machine Learning
AI Safety
Experimental Research
Python
Reinforcement Learning
Natural Language Processing (NLP)
Collaboration

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