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Salary: USD 320,000 - 405,000 / annual
The Encodings Infra team at Anthropic maintains the libraries that engineers and researchers use to encode text and multimodal data into a form that Claude can consume. This role also involves determining Claude's prompt shape: how a user's turn is represented to the model, how Claude calls tools and receives tool results, and related infrastructure.
As a Software Engineer on this team, you will own the design and maintenance of these libraries—keeping their APIs intuitive, their performance sharp, and their abstractions solid enough that most of the organization never has to think about encodings or prompt structures. Your work will enable Claude to learn new ways of understanding the world.
This role is unusually broad. Your work will touch systems across the codebase, from pretraining to finetuning to the API. You will collaborate closely with both researchers and engineers to ensure new encoding ideas can move quickly from experiment to production.
Key responsibilities include:
- Maintain and improve the encoding libraries used by engineers and researchers across Anthropic
- Run experiments to determine the optimal way to feed structured data into Claude
- Design data structures and abstractions that shield most of the organization from encoding details while enabling "power users"
- Adapt encoding libraries to support new research directions and ensure research ideas ship to production
- Optimize encoding performance across dependent systems
- Work with research teams to ship new multimodal data types (audio, video, etc.) to production
- Redesign core abstractions to enable changes in how data is encoded without breaking downstream teams
You should have strong communication skills, enjoy working closely with researchers and engineers, be results-oriented with a bias toward flexibility and impact, and care about the societal implications of your work.
REQUIREMENTS:
- 5+ years of software engineering experience, with meaningful time spent maintaining libraries, SDKs, or developer-facing APIs
- Familiarity with ML terminology and LLM architecture (you don't need to be an ML expert, but enough understanding to work effectively alongside researchers)
- Experience carrying out complex refactors in large codebases
- Strong communication skills
- Bachelor's degree or equivalent combination of education, training, and/or experience in a field relevant to the role
STRONG CANDIDATES MAY ALSO HAVE:
- Experience with tokenizers or other text/data encoding systems
- Experience maintaining a widely-used library over a long period
- Performance optimization expertise
- Python and/or Rust proficiency
- Reinforcement learning or model training infrastructure experience
