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

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 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. 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 (like those we would designate as ASL-3 or ASL-4 under our Responsible Scaling Policy), often in collaboration with other teams including Interpretability, Fine-Tuning, and the Frontier Red Team. Our blog provides an overview of topics that the Alignment Science team is either currently exploring or has previously explored. For the London team, we are opportunistically hiring for the following 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 prioritizing 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, like 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 The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. 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. But 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. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting th

Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience. Minimum years of experience: Relevant to internal job level requirements (not specified). Skills and experience sought: significant software, ML, or research engineering experience; some experience contributing to empirical AI research projects; some familiarity with technical AI safety research; prefer fast-moving collaborative projects; ability to pick up slack; care about the impacts of AI. Strong candidates may have: experience authoring research papers in machine learning, NLP, or AI safety; experience with LLMs; experience with reinforcement learning; experience with Kubernetes clusters and complex shared codebases. Visa sponsorship is available for this role.

Build and run elegant and thorough machine learning experiments to understand and steer the behavior of powerful AI systems. Conduct exploratory experimental research on AI safety, focusing on risks from powerful future systems. Collaborate with teams such as Interpretability, Fine-Tuning, and the Frontier Red Team. Work on defined research areas for the London team (AI Control; Alignment Stress-testing). Contribute ideas, figures, and writing to research papers, blog posts, and talks. Run experiments feeding into AI safety efforts like the Responsible Scaling Policy. Develop tooling to evaluate novel LLM-generated jailbreaks and write prompts to test models’ reasoning in safety-relevant contexts. Participate in multi-agent reinforcement learning experiments (e.g., AI Debate) and other safety-focused evaluations.