Research Engineer, Pretraining Scaling

About the Role

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 role lives at the boundary between research and engineering across our production training stack: performance optimization, hardware debugging, experimental design, and launch coordination.

Responsibilities

• 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

Qualifications

Minimum Qualifications

• Hands-on experience training large language models, or deep expertise with JAX, TPU, PyTorch, or large-scale distributed systems
• Enjoyment of both research and engineering work (approximate 50/50 split)
• Excitement about being on-call for production systems, working extended hours during launches, and solving hard problems under pressure
• Ability to debug complex, ambiguous problems across multiple layers of the stack
• Clear communication and effective collaboration skills across time zones and during high-stress incidents
• Care regarding the societal impacts of AI and responsible scaling

Preferred Qualifications

• Previous experience training LLMs or working extensively with JAX/TPU, PyTorch, or other ML frameworks at scale
• Contributions to open-source LLM frameworks (e.g., open_lm, llm-foundry, mesh-transformer-jax)
• Published research on model training, scaling laws, or ML systems
• Experience with production ML systems, observability tools, or evaluation infrastructure
• Background as a systems engineer, quant, or in other roles requiring both technical depth and operational excellence