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, you will ensure frontier models train reliably, efficiently, and at scale. This role sits between research and engineering, working across the entire production training stack: performance optimization, hardware debugging, experimental design, and launch coordination. You will own critical pipeline components, debug complex issues, design experiments to improve efficiency, respond to on‑call incidents, build monitoring dashboards, extend the codebase, collaborate with teams in SF and London, and document institutional knowledge.
Research Engineer, Pretraining Scaling - London at Anthropic
On-site - London, UK
More jobs at 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
- genuinely enjoy both research and engineering work with a roughly 50/50 split
- excited about being on‑call for production systems and working long days during launches
- thriving when working on what is most impactful, even if priorities change day to day
- excel at debugging complex, ambiguous problems across multiple layers of the stack
- communicate clearly and collaborate effectively, especially when coordinating across time zones or during high‑stress incidents
- 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 such as 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
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)observability toolsevaluation infrastructuretraining codebase
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