Research Engineer, Virtual Collaborator (Cowork)

Deadline to apply:

None. Applications will be reviewed on a rolling basis.

We require at least a Bachelor's degree in a related field or equivalent experience.

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.

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:

$500,000

—

$850,000 USD

Logistics

We are looking for a Machine Learning 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 environments that transform Claude into the best virtual collaborator, training on everything from navigating internal knowledge to creating financial models.

Designing and implementing reinforcement learning pipelines specifically 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 authentic training environments

Developing robust rubric-based evaluation systems that maintain quality while avoiding reward hacking

Training Claude on advanced document manipulation, including understanding, enhancing, and co-creating

Partnering directly with product teams to ensure training aligns with shipped features

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.

Strong candidates may also have experience with:

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