Research Engineer, Pretraining

Anthropic Anthropic · AI Frontier · London, United Kingdom · AI Research & Engineering

Research Engineer focused on pretraining large language models, involving research into model architecture, algorithms, data processing, and optimizers, along with scaling training infrastructure and analyzing experiments. The role contributes to the entire stack from low-level optimizations to high-level model design.

What you'd actually do

  1. Conduct research and implement solutions in areas such as model architecture, algorithms, data processing, and optimizer development
  2. Independently lead small research projects while collaborating with team members on larger initiatives
  3. Design, run, and analyze scientific experiments to advance our understanding of large language models
  4. Optimize and scale our training infrastructure to improve efficiency and reliability
  5. Develop and improve dev tooling to enhance team productivity

Skills

Required

  • Python
  • deep learning frameworks
  • large-scale machine learning
  • language models
  • software engineering

Nice to have

  • high-performance, large-scale ML systems
  • GPUs
  • Kubernetes
  • OS internals
  • transformer architectures
  • reinforcement learning techniques
  • large-scale ETL processes

What the JD emphasized

  • Advanced degree (MS or PhD) in Computer Science, Machine Learning, or a related field
  • Strong software engineering skills with a proven track record of building complex systems
  • Expertise in Python and experience with deep learning frameworks (PyTorch preferred)
  • Familiarity with large-scale machine learning, particularly in the context of language models
  • Ability to balance research goals with practical engineering constraints

Other signals

  • developing the next generation of large language models
  • research and implement solutions in areas such as model architecture, algorithms, data processing, and optimizer development
  • Design, run, and analyze scientific experiments to advance our understanding of large language models
  • Optimize and scale our training infrastructure
  • Contribute to the entire stack, from low-level optimizations to high-level model design