Research Engineer, Virtual Collaborator (Cowork)
Anthropic · New York City, NY | San Francisco, CA | Seattle, WA · AI Research & Engineering
About this role
Anthropic is hiring a mid-level Research Scientist in the machine learning function based in New York City, NY | San Francisco, CA | Seattle, WA. The posting calls out experience with Python, Reinforcement Learning, API Development, ETL. Compensation is listed at $500,000–$850,000 per year.
- Role
- Research Scientist
- Function
- machine learning
- Level
- mid
- Track
- Individual contributor
- Employment
- Full-time
- Location
- New York City, NY | San Francisco, CA | Seattle, WA
- Department
- AI Research & Engineering
More roles at Anthropic
Job description
from Anthropic careersAbout Anthropic
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
We are looking for a Research Engineer to help us train Claude specifically for virtual collaborator workflows. While Claude excels at general tasks, a lot of knowledge work requires targeted training on real organizational data and workflows. Your job will be to design and implement reinforcement learning (RL) environments that transform Claude into the best virtual collaborator, training on realistic tasks from navigating internal knowledge to creating financial models.
Responsibilities:
- Training Claude on document manipulation with good taste, including understanding, enhancing, and co-creating (e.g., Office doc formats, data visualization)
- Designing and implementing reinforcement learning pipelines targeted at virtual collaborator use cases (productivity, organizational navigation, vertical domains)
- Building and scaling our data creation platform for generating high-quality, open-ended tasks with domain experts and crowdworkers Integrating real organizational data to create realistic training environments
- Developing robust evaluation systems that maintain quality while avoiding reward hacking
- Partnering directly with product teams (e.g., Cowork, claude.ai) to ensure training aligns with product features