Anthropic’s ML Performance and Scaling team trains production pretrained models. As a Research Engineer on this team, you’ll ensure frontier models train reliably, efficiently, and at scale. The role sits between research and engineering, working across the entire production training stack: performance optimization, hardware debugging, experimental design, launch coordination, and responding to production incidents. You’ll own critical aspects of the pretraining pipeline, debug complex stack issues, design experiments to improve training efficiency, and build production logging, monitoring dashboards, and evaluation infrastructure.
Research Engineer, Pretraining Scaling - London na Anthropic
Presencial - London, UK
Ver mais vagas na AnthropicSalary
GBP 260,000 - 630,000
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 (50/50 split)
- Experience being on‑call for production systems, working long days during launches
- Thriving in high‑pressure, dynamic environments
- Excellent debugging skills across multiple stack layers
- Strong communication and collaboration across time zones
- Passion for responsible AI scaling and societal impacts
- Bachelor’s degree or equivalent education/training
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 (e.g., open_lm, llm-foundry, mesh‑transformer‑jax)Logging and monitoring dashboardsEvaluation infrastructure
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