Research Engineer, Virtual Collaborator (Cowork)

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 organisational 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 visualisation)

• Designing and implementing reinforcement learning pipelines targeted at virtual collaborator use cases (productivity, organisational navigation, vertical domains)

• Building and scaling our data creation platform for generating high-quality, open-ended tasks with domain experts and crowdworkers

• Integrating real organisational 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 modelling 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 $500,000-$850,000 USD.