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
• Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience
• 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

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
• Location:This role requires working in-office 5 days per week in San Francisco

• Deadline to apply: None. Applications will be reviewed on a rolling basis