Anthropic's ML Performance and Scaling team trains production pretrained models, which directly shapes the company's future and its mission to build safe, beneficial AI systems. The role aims to ensure frontier models train reliably, efficiently, and at scale, addressing challenges in performance optimization, hardware debugging, experimental design, and launch coordination.
Requirements
- Have hands-on experience training large language models, or deep expertise with JAX, TPU, PyTorch, or large-scale distributed systems
- Previous experience training LLM’s or working extensively with JAX/TPU, PyTorch, or other ML frameworks at scale
- Contributed 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
- Excel at debugging complex, ambiguous problems across multiple layers of the stack
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
- Contribute to the team's institutional knowledge by documenting systems, debugging approaches, and lessons learned
Other
- 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
- 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
