Research Engineer, Anthropic

• Have hands-on experience training large language models, or deep expertise with JAX, TPU, PyTorch, or large-scale distributed systems
• Genuinely enjoy both research and engineering work—you'd describe your ideal split as roughly 50/50 rather than heavily weighted toward one or the other
• Are excited about being on-call for production systems, working long days during launches, and solving hard problems under pressure
• Thrive when working on whatever is most impactful, even if that changes day-to-day based on what the production model needs
• Excel at debugging complex, ambiguous problems across multiple layers of the stack
• Communicate clearly and collaborate effectively, especially when coordinating across time zones or during high-stress incidents
• Are passionate about the work itself and want to refine your craft as a research engineer
• Care about the societal impacts of AI and responsible scaling
• We require at least a Bachelor's degree in a related field or equivalent experience

Desirable

• 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
• During launches, the team works in tight lockstep, responding to production issues that can't wait for tomorrow
• Own critical aspects of our production pretraining pipeline, including model operations, performance optimization, observability, and reliability
• Debug and resolve complex issues across the full stack—from hardware errors and networking to training dynamics and evaluation infrastructure
• Design and run experiments to improve training efficiency, reduce step time, increase uptime, and enhance model performance
• Respond to on-call incidents during model launches, diagnosing problems quickly and coordinating solutions across teams
• Build and maintain production logging, monitoring dashboards, and evaluation infrastructure
• Add new capabilities to the training codebase, such as long context support or novel architectures
• Collaborate closely with teammates across SF and London, as well as with Tokens, Architectures, and Systems teams
• Contribute to the team's institutional knowledge by documenting systems, debugging approaches, and lessons learned
• 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
• You'll work alongside world-class researchers and engineers, and the institutional knowledge you build will compound in ways that can't be easily transferred
• For people who thrive on this type of work, it's uniquely rewarding. We're building a close-knit team of people who genuinely care about doing excellent work together

• Applications will be reviewed on a rolling basis