Anthropic's ML Performance and Scaling team trains our production pretrained models, shaping the company's future and mission to build safe, beneficial AI systems. As a Research Engineer on this team, you will ensure our frontier models train reliably, efficiently, and at scale. The role sits at the intersection of research and engineering, working across the entire production training stack—performance optimization, hardware debugging, experimental design, and launch coordination. You will own critical aspects of the pretraining pipeline, debug and resolve complex issues, design experiments to improve efficiency and performance, respond to on‑call incidents during launches, and build and maintain logging, monitoring, and evaluation infrastructure. Collaboration with teammates across San Francisco and London, as well as Tokens, Architectures, and Systems teams, will be key. The position requires in‑office work in San Francisco 5 days a week, offering a competitive salary range of $350,000–$850,000 USD, equity donation matching, generous vacation and parental leave, flexible working hours, and a collaborative office environment.
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
- Experience debugging complex, ambiguous problems across multiple layers of the stack
- Experience with production ML systems, observability tools, or evaluation infrastructure
- Excellent communication and collaboration skills
- Passion for responsible AI and societal impacts
- Willingness to be on‑call for production systems
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
Technologies
JAXTPUPyTorchLarge‑scale distributed systemsObservability toolsEvaluation infrastructureopen_lmllm‑foundrymesh‑transformer‑jax
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