[Expression of Interest] Research Engineer / Scientist, Alignment - London
- Salary
- £260,000 - £370,000
- Level
- Mid
- Visa
- Sponsors visas
Not stated in the posting: work type.
Open. First seen 7 March 2026.
About the role
Anthropic's standard text
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.
Anthropic's standard text
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 the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work.
We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.
Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.
How we're different
Anthropic's standard text
We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles.
We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.
The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
Come work with us!
Anthropic is a public benefit corporation headquartered in San Francisco. 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.
What this role asks for
Particular to this role
Kubernetes · NLP
Rare in Anthropic's other 299 open postings.
Standard for software engineering roles
Python
Common in software engineering postings at any company.
63% of this description is written for the role. The rest is Anthropic's standard text, folded away below.
Read against Anthropic's other open postings on this site.
The team: AI Research & Engineering HIRES STEADILY
This role is one of 55 open in AI Research & Engineering. 4 were posted in the last 30 days and the oldest in April: a team that hires all year rather than one being built.
WHAT THE TEAM WORKS WITH, ACROSS ITS POSTINGS
LLM (39 of 55), Machine Learning (39 of 55), Python (32 of 55), PyTorch (15 of 55), Kubernetes (12 of 55), Rust (10 of 55), C++ (7 of 55)
OTHER ROLES ON THIS TEAM
- Staff Research Engineer, Multi-Agent Scaling posted Oct 2
- Software Engineer, Sandboxing posted Oct 1
- Research Engineer / Research Scientist, RL Frontiers posted Sep 29
- Research Engineer / Performance Engineer, RL Distributed Systems posted Sep 29
- Software Engineer, Tokens and Prompt Structures posted Sep 16
- Staff Software Engineer, Search posted Sep 11
- Performance Engineer, Inference Engine posted Sep 9
- Product Manager, Claude Science posted Aug 31
- Staff+ Research Engineer, RL Data Platform posted Aug 27
- Engineering Manager, Search posted Aug 3
- Staff Software Engineer, Code RL posted Jul 25
- Staff Software Engineer, Environments Infrastructure posted Jul 24
- Machine Learning Infrastructure Engineer, Safeguards Research posted Jul 21
- Research Engineer, Chip Design RL (Reinforcement Learning) posted Jul 13
- Engineering Manager, Research Data Platform posted Jul 7
- Software Engineer, Research Productivity posted Jul 6
- Research Engineer, Life Sciences posted Jul 3
- Research Engineer, Computer Use posted Jun 30
- Research Engineer, RL Scaling Science posted Jun 22
- Research Engineer, Code RL (Reinforcement Learning) posted Jun 11
- Data Operations Manager, Human Data posted Jun 3
- Product Engineer, Computer Use posted Jun 2
- Engineering Manager, Research Productivity posted May 22
- Performance Engineer, Inference Systems posted May 20
- Research Engineer, Post-Training Model Evaluations posted Apr 28
- Research Engineer, Machine Learning (RL Velocity) posted Apr 23
- Research Engineer, Machine Learning (RL Velocity) posted Apr 23
- Full-Stack Software Engineer, Reinforcement Learning posted Apr 14
- Anthropic Fellows Program, ML Systems & Reinforcement Learning posted Apr 10
- Anthropic Fellows Program, AI Safety & Security posted Apr 10
- Research Engineer, Pretraining posted Feb 17
- Research Engineer, Production Model Post-Training posted Feb 12
- Research Engineer, Machine Learning (Reinforcement Learning) posted Feb 11
- Research Engineer, Visual Knowledge Work posted Jan 16
- Research Engineer, Cybersecurity RL (Reinforcement Learning) posted Dec 9
- Software Engineer, Research Tools posted Nov 8
- Research Engineer, Interpretability posted Nov 7
- Pre-training Data Infrastructure Engineer posted Nov 3
- Research Engineer, RL Engineering posted Oct 15
- Research Engineer / Research Scientist, Tokens posted Oct 10
- ML/Research Engineer, Safeguards posted Oct 9
- Research Engineer, Pretraining Scaling - London posted Sep 30
- Research Engineer, Pretraining Scaling posted Sep 30
- Performance Engineer, GPU posted Sep 22
- Research Engineer/Research Scientist, Pre-training posted Jul 18
- Engineering Manager, GPU (ML Accelerator) posted May 29
- TPU Kernel Engineer posted May 22
- Research Engineer, Discovery posted May 14
- Staff Research Engineer, Discovery Team posted Apr 23
- Research Engineer / Scientist, Alignment posted Apr 14
- Research Engineer, Production Model Post-Training posted Apr 1
- Research Engineer, Machine Learning (Reinforcement Learning) posted Apr 1
- Research Engineer, Knowledge Team posted Apr 23
- Performance Engineer posted Apr 22
From the team and dates on Anthropic's own job board.
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