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Anthropic · London · Hybrid
Research Engineer, Science of Scaling
3/6/2026
Description
About the role
Anthropic is seeking a Research Engineer/Scientist to join the Science of Scaling team, responsible for developing the next generation of large language models. In this role, you will work at the intersection of cutting-edge research and practical engineering, contributing to the development of safe, steerable, and trustworthy AI systems. You'll contribute across the entire stack, from low-level optimizations to high-level algorithm and experimental design, balancing research goals with practical engineering constraints.
Responsibilities:
Conduct research intro the science of converting compute into intelligence
Independently lead small research projects while collaborating with team members on larger initiatives
Design, run, and analyze scientific experiments to advance our understanding of large language models
Optimize training infrastructure to improve efficiency and reliability
Develop dev tooling to enhance team productivity
Qualifications
Have significant software engineering experience and a proven track record of building complex systems
Hold an advanced degree (MS or PhD) in Computer Science, Machine Learning, or a related field
Are proficient in Python and experienced with deep learning frameworks
Are results-oriented with a bias towards flexibility and impact
Enjoy pair programming and collaborative work, and are willing to take on tasks outside your job description to support the team
View research and engineering as two sides of the same coin, seeking to understand all aspects of the research program to maximize impact
Care about the societal impacts of your work and have ambitious goals for AI safety and general progress
Experience with JAX
Experience with reinforcement learning
Experience working on high-performance, large-scale ML systems
Familiarity with accelerators, Kubernetes, and OS internals
Experience with language modeling using transformer architectures
Background in large-scale ETL processes
Experience with distributed training at scale (thousands of accelerators)
Experience in all of the above areas — we value breadth of interest and willingness to learn over checking every box
Prior work specifically on language models or transformers; strong engineering fundamentals and ML knowledge transfer well
An advanced degree — exceptional engineers with strong research instincts are equally encouraged to apply