Anthropic’s ML Performance and Scaling team trains production pretrained models, focusing on ensuring frontier models train reliably and efficiently at scale. As a Research Engineer, you will own critical aspects of the production pretraining pipeline, debug complex issues, design experiments, respond to on‑call incidents, build logging and monitoring, add new capabilities, and collaborate across teams in San Francisco and London.
Research Engineer, Pretraining Scaling at Anthropic
On-site - San Francisco, CA, United States
More jobs at AnthropicSalary
USD 350,000 - 850,000
Requirements
Skills
- Hands‑on experience training large language models
- Deep expertise with JAX, TPU, PyTorch, or large‑scale distributed systems
- Enjoy both research and engineering work
- Excited about being on‑call for production systems and working long days during launches
- Thrive when working on the most impactful tasks
- Excel at debugging complex, ambiguous problems across multiple layers of the stack
- Communicate clearly and collaborate effectively
- 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
- Previous experience training LLMs 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 technical depth and operational excellence
- Bachelor’s degree or equivalent education, training, or experience
Responsibilities
- Own critical aspects of the 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
Technologies
JAXTPUPyTorchLarge‑scale distributed systemsOpen‑source LLM frameworks (open_lm, llm‑foundry, mesh‑transformer‑jax)Production ML systemsObservability toolsEvaluation infrastructure
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