Tech Stack
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Agentic Systems @ 3
Communication @ 6
Machine Learning @ 3
Python @ 5
Reinforcement Learning @ 3
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
Anthropic’s Computer Use team focuses on teaching Claude to see, use, and understand computer interfaces. The role involves advancing the models’ ability to reliably and safely operate real software, with work translating directly into model improvements for Anthropic’s products and customers.
Responsibilities
- Design and run experiments to improve Claude’s perception and agentic capabilities.
- Develop robust, reliable evaluation frameworks for measuring models’ ability to complete complex computer tasks.
- Build and improve computer use and vision reinforcement learning training environments.
- Create pipelines and tools to test and validate complex reinforcement learning environments.
- Collaborate with teams across the model training and infrastructure stack to improve the production training setup.
- Partner with product teams to bring research advances into production.
Requirements
Minimum Qualifications
- Software engineering experience and proficiency in Python.
- Experience training, fine-tuning, or evaluating machine learning models.
- Strong communication skills and a collaborative working style.
- Care about the societal impacts and safety of the work.
Preferred Qualifications
- Experience training models for computer use or other agentic capabilities.
- Experience with reinforcement learning, particularly in long-horizon or sparse-reward settings.
- Familiarity with multimodal model training.
- Experience building evaluations or benchmarks for agentic systems.
- Experience building reinforcement learning environments, simulation systems, or large-scale machine learning infrastructure.
- Experience working closely with product teams to drive model improvements.
Education and Experience
- Bachelor’s degree or an equivalent combination of education, training, and/or experience.
- A field of study relevant to the role, as demonstrated through coursework, training, or professional experience.
- Years of experience required will correlate with the internal job-level requirements for the position.
Benefits
Anthropic offers competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and office space for collaboration.
Work Policy and Sponsorship
Staff are currently expected to work from one of Anthropic’s offices at least 25% of the time, although some roles may require more time in the office. Anthropic sponsors visas and states that it will make every reasonable effort to obtain a visa for candidates who receive an offer, with support from an immigration lawyer.