Staff Machine Learning Engineer, Virtual Collaborator

Anthropic•San Francisco, CA

15d•$340,000 - $560,000•Hybrid

About The Position

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. 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.

Requirements

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

Nice To Haves

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

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