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Salary: USD 320,000 - 405,000 / annual
Anthropic is seeking a Software Engineer to join the Encodings Infra team, which maintains the libraries used across the organization to encode text and multimodal data into forms that Claude can consume. This team also determines Claude's prompt shape—how user turns are represented to the model, how Claude calls tools and receives results, and related infrastructure.
In this role, you will own the design and maintenance of encoding libraries, ensuring their APIs remain intuitive, performance stays sharp, and abstractions are solid enough that most of the organization never needs to think about the underlying encoding details. Your work will directly enable Claude to learn new ways of understanding the world.
The role is unusually broad in scope. Your work will touch systems across the codebase, from pretraining to finetuning to the API layer. You'll 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 encoding libraries used by engineers and researchers across Anthropic
- Run experiments to determine optimal ways to feed structured data into Claude
- Design data structures and abstractions that shield the organization from encoding complexity while enabling power users
- Adapt encoding libraries to support new research directions and ensure research ideas can ship to production
- Optimize encoding performance across dependent systems
Representative projects include working with research teams to ship new multimodal data types (audio, video, etc.) to production, and redesigning core abstractions to enable encoding changes without breaking downstream teams.
Requirements:
- 5+ years of software engineering experience with meaningful time maintaining libraries, SDKs, or developer-facing APIs
- Familiarity with ML terminology and LLM architecture (not required to be an ML expert, but sufficient understanding to work effectively with researchers and interpret experiment results)
- Experience carrying out complex refactors in large codebases
- Strong communication skills and comfort working closely with researchers and engineers
- Results-oriented mindset with bias toward flexibility and impact
- Willingness to take on work outside formal job description
- Care about societal impacts of AI work
Desirable experience:
- Tokenizers or other text/data encoding systems
- Maintaining widely-used libraries over extended periods
- Performance optimization
- Python and/or Rust
- Reinforcement learning or model training infrastructure
Minimum education: Bachelor's degree or equivalent combination of education, training, and professional experience in a field relevant to the role.
Location-based hybrid policy: Currently expects all staff in office at least 25% of the time, though some roles may require more.
Visa sponsorship available.
