Staff Machine Learning Engineer, Virtual Collaborator
New York City, NY; San Francisco, CA; Seattle, WAFull-TimeStaffAI / Data Science
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.
How we're different
- We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact — advancing our long-term goals of steerable, trustworthy AI — rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.
- The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
