Anthropic· AI Research & Engineering· London, UK
Research Engineer, Science of Scaling
Classified Tasks (12)
Automate 0%Augment 67%Human-Only 33%
Augment (8)
AI assists, human decides
Design scientific experiments to advance understanding of large language models
analytical
Run scientific experiments to advance understanding of large language models
operational
Analyze results from scientific experiments to draw conclusions about large language models
analytical
Optimize training infrastructure to improve efficiency and reliability
technical
Develop developer tooling to enhance team productivity
technical
Implement low-level optimizations across the stack to improve model performance
technical
Design high-level algorithms and experimental frameworks for large language model research
creative
Contribute to the development of safe, steerable, and trustworthy AI systems
technical
Human-Only (4)
Requires human judgment
Conduct research into the science of converting compute into intelligence
creative
Independently lead small research projects
leadership
Collaborate with team members on larger research and engineering initiatives
communication
Balance research objectives with practical engineering constraints during system design
operational
Job description
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems. 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 You may be a good fit if you: 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 Strong candidates may have: 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) Strong candidates need not have: 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 The annual compensation range for this role is listed below. For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. <div class="tit