Software Engineer, Encoding Libraries

Anthropic•San Francisco, CA

1d•Hybrid

About The Position

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: The Encodings Infrastructure team maintains the libraries that engineers and researchers across Anthropic use to encode text and multimodal data into a form that Claude can consume. As a Software Engineer on this team, you'll own the design and maintenance of these libraries — keeping their APIs intuitive, their performance sharp, and their abstractions solid enough that most of the org never has to think about encoding at all. You’ll have the satisfaction of knowing that your work enabled 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, and you'll collaborate closely with both researchers and engineers to make sure new encoding ideas can move quickly from experiment to production.

Requirements

Have 5+ years of software engineering experience, with meaningful time spent maintaining libraries, SDKs, or developer-facing APIs
Have familiarity with ML terminology and LLM architecture — you don't need to be an ML expert, but enough understanding to work effectively alongside researchers
Have experience carrying out complex refactors in large codebases
Have strong communication skills and enjoy working closely with researchers and engineers to understand what they need
Are results-oriented, with a bias towards flexibility and impact
Pick up slack, even if it goes outside your job description
Care about the societal impacts of your work

Nice To Haves

Tokenizers or other text/data encoding systems
Maintaining a widely-used library over a long period of time
Performance optimization
Python and/or Rust
Reinforcement learning or model training infrastructure

Responsibilities

Maintain and improve the encoding libraries used by engineers and researchers across Anthropic, with a focus on clean, user-friendly APIs
Design data structures and abstractions that shield most of the organization from the details of how encoded data works while enabling “power users”
Adapt the encoding libraries to support new research directions as they emerge, and make sure that we can ship these research ideas to production
Optimize encoding performance across the systems that depend on these libraries
Work across Anthropic's codebase to improve how encoded and unencoded data is handled