6 months ago
Responsibilities
- Conduct research and implement solutions involving model architecture, algorithms, data processing, and optimizer development.
- Independently lead small research projects and collaborate on larger initiatives.
- Design, run, and analyze scientific experiments involving large language models.
- Optimize and scale training infrastructure for efficiency and reliability.
- Develop and improve developer tooling to increase team productivity.
- Contribute across the stack, from low-level optimizations to high-level model design.
Requirements
- At least a bachelor's degree in a related field or equivalent experience; an advanced degree in Computer Science, Machine Learning, or a related field is listed as a qualification.
- Strong software engineering skills with a proven record of building complex systems.
- Expertise in Python and experience with deep learning frameworks, preferably PyTorch.
- Familiarity with large-scale machine learning, particularly language models.
- Ability to balance research goals with practical engineering constraints.
- Preferred experience includes high-performance large-scale ML systems, GPUs, Kubernetes, operating-system internals, transformer-based language modeling, reinforcement learning, and large-scale ETL processes.
- Strong problem-solving, communication, collaboration, and research-engineering skills are expected.
Benefits
- Annual salary range of £260,000—£630,000 GBP
- Hybrid policy requiring staff to work from an office at least 25% of the time
- Visa sponsorship may be available
- Competitive benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and office collaboration space
Tech Stack
Categories
About Anthropic
We're an AI research company that builds reliable, interpretable, and steerable AI systems. Our first product is Claude, an AI assistant for tasks at any scale. Our research interests span multiple areas including natural language, human feedback, scaling laws, reinforcement learning, code generation, and interpretability.