The opportunity
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.
What you'll do
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
What they're looking for
- 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
- 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
