Research Engineer, Science in London

About AnthropicAnthropic'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. Anthropic's ML Performance and Scaling team trains our production pretrained models, work that directly shapes the company's future and our mission to build safe, beneficial AI systems. As a Research Engineer on this team, you'll ensure our frontier models train reliably, efficiently, and at scale. This is demanding, high-impact work that requires both deep technical expertise and a genuine passion for the craft of large-scale ML systems. This role lives at the boundary between research and engineering. You'll work across our entire production training stack: performance optimization, hardware debugging, experimental design, and launch coordination. Own critical aspects of our production pretraining pipeline, including model operations, performance optimization, observability, and reliabilityDebug and resolve complex issues across the full stack—from hardware errors and networking to training dynamics and evaluation infrastructureDesign and run experiments to improve training efficiency, reduce step time, increase uptime, and enhance model performanceRespond to on-call incidents during model launches, diagnosing problems quickly and coordinating solutions across teamsBuild and maintain production logging, monitoring dashboards, and evaluation infrastructureAdd new capabilities to the training codebase, such as long context support or novel architecturesCollaborate closely with teammates across SF and London, as well as with Tokens, Architectures, and Systems teamsContribute to the team's institutional knowledge by documenting systems, debugging approaches, and lessons learnedYou May Be a Good Fit If You: Have hands-on experience training large language models, or deep expertise with JAX, TPU, PyTorch, or large-scale distributed systemsGenuinely enjoy both research and engineering work—you'd describe your ideal split as roughly 50/50 rather than heavily weighted toward one or the otherAre excited about being on-call for production systems, working long days during launches, and solving hard problems under pressureThrive when working on whatever is most impactful, even if that changes day-to-day based on what the production model needsExcel at debugging complex, ambiguous problems across multiple layers of the stackCommunicate clearly and collaborate effectively, especially when coordinating across time zones or during high-stress incidentsAre passionate about the work itself and want to refine your craft as a research engineerCare about the societal impacts of AI and responsible scalingStrong Candidates May Also Have: Previous experience training LLM's or working extensively with JAX/TPU, PyTorch, or other ML frameworks at scaleContributed to open-source LLM frameworks (e.g., open_lm, llm-foundry, mesh-transformer-jax)Published research on model training, scaling laws, or ML systemsExperience with production ML systems, observability tools, or evaluation infrastructureBackground as a systems engineer, quant, or in other roles requiring both technical depth and operational excellenceWhat Makes This Role Unique: This is not a typical research engineering role. The work is highly operational—you'll be deeply involved in keeping our production models training smoothly, which means being responsive to incidents, flexible about priorities, and comfortable with uncertainty. During launches, the team often works extended hours and may need to respond to issues on evenings and weekends. However, this operational intensity comes with extraordinary learning opportunities. You'll gain hands-on experience with some of the largest, most sophisticated training runs in the industry. We're building a close-knit team of people who genuinely care about doing excellent work together. If you're someone who wants to be part of training the models that will define the future of AI—and you're excited about the full reality of what that entails—we'd love to hear from you. This role requires working in-office 5 days per week in London. 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. Bachelor's degree or an equivalent combination of education, training, and/or experienceRequired field of study: A field relevant to the role as demonstrated through coursework, training, or professional experienceMinimum years of experience: Years of experience required will correlate with the internal job level requirements for the positionLocation-based hybrid policy: 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. 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. How we're differentWe 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. 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. 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.