Research Engineer, Virtual Collaborator (Cowork) at Anthropic — Remote North America

About 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

You may be a good fit if you:

Are a very experienced Python programmer who can quickly produce reliable, high quality code that your teammates love using

Have 5-8 years of strong machine learning experience

Thrive at the intersection of research and product, with a pragmatic approach to solving real-world problems

Are comfortable with ambiguity and can balance research rigor with shipping deadlines

Enjoy collaborating across multiple teams (data operations, model training, product)

Can context-switch between research problems and product engineering tasks

Care about making AI genuinely helpful for everyday enterprise workflows

Strong candidates may also have experience with:

Creating RL envs for realistic tasks.

Reward modeling and preventing reward hacking

Building human-in-the-loop training systems or crowdsourcing platforms

Working with enterprise tools and APIs (Google Workspace, Microsoft Office, Slack, etc.)

Developing evaluation frameworks for open-ended tasks

Domain expertise in finance, legal, or healthcare workflows

Creating scalable data pipelines with quality control mechanisms

Translating product requirements into technical training objectives

Deadline to apply: None. Applications will be reviewed on a rolling basis.

The annual compensation range for this role is listed below.