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Software Engineer (Tokens and Prompt Structures)

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Onsite

Software Engineer (Tokens and Prompt Structures)

Anthropic

Software Engineer (Tokens and Prompt Structures)

Skills

RustPythonMachine Learning

17 days ago

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Important information


Contract type:

Permanent

Salary:

$320,000 – $405,000 / year

Location:

New York, NY, USA

Work mode:

Onsite

Published on:

4 October 2026

What they need


  • The Encodings Infra team maintains the libraries that engineers and researchers across Anthropic use to encode text and multimodal data into a form that Claude can consume
  • It also determines Claude’s prompt shape: how a user’s turn is represented to the model, how Claude calls tools and receives tool results, and so on
  • 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 encodings or prompt structures 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
  • 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 without confusing it
  • 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
  • Representative projects:
  • Working with a research team to ship a new multimodal data type (audio, video, etc) to production
  • Redesigning a core abstraction so that we can change how data is encoded into Claude without breaking downstream teams

Benefits

  • Comprehensive health, dental, and vision insurance for you and your dependents
  • Inclusive fertility benefits via Carrot Fertility
  • 22 weeks of paid parental leave
  • Flexible paid time off and absence policies
  • Mental health support for you and your dependents
  • Competitive salary and equity packages
  • Optional equity donation matching at a 1:1 ratio, up to 25% of your equity grant
  • Retirement plans with competitive matching
  • Life and income protection plans
  • $500/month flexible wellness and time saver stipend
  • Commuter benefits
  • Annual education stipend
  • Home office stipends
  • Relocation support for those moving for Anthropic
  • Daily meals and snacks in the office- Are results-oriented, with a bias towards flexibility and impact
  • Pick up slack, even if it goes outside your job description
  • Have experience carrying out complex refactors in large codebases
  • Care about the societal impacts of your work
  • Have strong communication skills and enjoy working closely with researchers and engineers to understand what they need
  • 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 and understand the results of experiments
  • We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you’re interested in this work
  • Reinforcement learning or model training infrastructure
  • Performance optimization
  • Python and/or Rust
  • Tokenizers or other text/data encoding systems
  • Maintaining a widely-used library over a long period of time

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