Staff Machine Learning Engineer, Virtual Collaborator

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 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. 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 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: 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 Deadline to apply: None. Applications will be reviewed on a rolling basis. The annual compensation range for this role is listed below. 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 Education requirements: 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. Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this. We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exc ... (truncated, view full listing at source)