About the role: We are looking for Research Engineers 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. Responsibilities: 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 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 strong machine learning research experience, particularly in reinforcement learning and fine-tuning 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 will 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 Domain expertise in finance, legal, or healthcare workflows Creating scalable data pipelines with quality control mechanisms Reward modeling and preventing reward hacking in RL systems Translating product requirements into technical training objectives

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