Software Engineer, Tokens and Prompt Structures

About the Role

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.

Responsibilities

• 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

You may be a good fit if you:

• 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
• 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

Strong candidates may also have experience with:

• 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

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

The annual compensation range for this role is listed below.

For sales roles, the range provided is the role's On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

Annual Salary

$320,000-$405,000 USD

Logistics

Minimum education: Bachelor's degree or an equivalent combination of education, training, and/or experience

Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience

Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position

Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.

Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.

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. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.