Research Engineer, Pretraining Scaling

• Train Anthropic's production pretrained models on the ML Performance and Scaling team — work that directly shapes the company's future and mission to build safe, beneficial AI. • Work across the entire production training stack: performance optimisation, hardware debugging, experimental design, and launch coordination. • Operate at the boundary between research and engineering, with an ideal split of roughly 50/50. • Note: this role requires working in-office 5 days per week in London.

• Have hands-on experience training large language models, or deep expertise with JAX, TPU, PyTorch, or large-scale distributed systems. • Enjoy both research and engineering work in roughly equal measure. • Be 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. • Be 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.

• Have previous experience training LLMs or working extensively with JAX/TPU, PyTorch, or other ML frameworks at scale. • Have contributed to open-source LLM frameworks such as open_lm, llm-foundry, or mesh-transformer-jax. • Have published research on model training, scaling laws, or ML systems. • Bring experience with production ML systems, observability tools, or evaluation infrastructure. • Have a background as a systems engineer, quant, or in other roles requiring both technical depth and operational excellence.

• Own critical aspects of the production pretraining pipeline, including model operations, performance optimisation, 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 San Francisco and London, as well as with Tokens, Architectures, and Systems teams. • Contribute to institutional knowledge by documenting systems, debugging approaches, and lessons learned.

• Receive competitive compensation between £260,000 and £630,000 GBP annually. • Get visa sponsorship where possible — Anthropic retains an immigration lawyer to support this. • Take generous parental leave. • Enjoy generous vacation and flexible working hours (outside of launch periods). • Optional equity donation matching. • Gain hands-on experience with some of the largest, most sophisticated training runs in the industry.

Anthropic's mission is to create reliable, interpretable, and steerable AI systems. They want AI to be safe and beneficial for users and society as a whole. The team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. Anthropic is a public benefit corporation offering competitive compensation and benefits.