10 hours ago
Base Salary
$320k - $405k/yr
Responsibilities
- Maintain and improve encoding libraries used across Anthropic.
- Run experiments to determine effective ways to represent structured data for Claude.
- Design data structures and abstractions that hide encoding details while supporting advanced users.
- Adapt encoding libraries for new research directions and move research ideas into production.
- Optimize encoding performance across dependent systems.
- Collaborate with researchers and engineers on encoding and prompt-structure needs.
Requirements
- 5+ years of software engineering experience, including meaningful experience maintaining libraries, SDKs, or developer-facing APIs.
- Familiarity with ML terminology and LLM architecture sufficient to work with researchers and interpret experiment results.
- Experience carrying out complex refactors in large codebases.
- Strong communication and collaboration skills.
- Preferred experience with tokenizers or other text/data encoding systems, maintaining widely used libraries, performance optimization, Python, Rust, reinforcement learning, or model training infrastructure.
- Bachelor’s degree or an equivalent combination of education, training, and/or experience in a relevant field demonstrated through coursework, training, or professional experience.
Benefits
- Annual salary range of $320,000—$405,000 USD.
- Visa sponsorship is available, with reasonable efforts made after an offer and support from an immigration lawyer.
- Hybrid policy requiring staff to be in an office at least 25% of the time, with some roles requiring more office time.
- Competitive benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and office collaboration space.
About Anthropic
Anthropic builds large language models and the Claude AI assistant for developers and enterprises, offered via API access and enterprise plans. Founded in 2021 and headquartered in San Francisco, it distributes Claude through its own platform and via partners such as Amazon Bedrock and Google Cloud’s Vertex AI. Its work emphasizes model reliability, interpretability, and practical tooling for tasks like coding assistance, analysis, and customer support automation.