Software Engineer, Human Data Interface

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

Anthropic's Human Data Interfaces team builds the systems that collect data to improve our models. This includes novel interfaces for data vendors, tooling, and front-end and back-end infrastructure that enables researchers to gather high-quality data at scale. As a Software Engineer, you'll own the architecture and execution of our data collection pipelines — designing systems that are both performant at scale and resilient to the rapidly changing needs of our research teams. You'll work closely with researchers, our cross-functional data operations partners, and the crowdworkers and vendors who use these tools day-to-day.

Responsibilities:

Architect and build data collection pipelines that support rapid iteration, balancing data quality and system maintainability

Think deeply about the experience of the crowdworkers and vendors using these systems, building interfaces that are clear, efficient, and lead to high-quality data

Collaborate closely with research teams to understand evolving data needs and iterate quickly on collection methods

Partner with our Human Data Operations team to understand the end-to-end data workflow and design interfaces that make their jobs easier

Prioritize and juggle multiple workstreams, making trade-off decisions in a fast-moving environment where research priorities can shift quickly

You May Be a Good Fit If You:

Believe that advanced AI systems could have a transformative effect on the world and are interested in helping make sure that transformation goes well

Are a strong full-stack engineer with broad experience across the stack

Are very good at building internal tools, including working with users of the tools to understand their needs

Thrive in fast-moving environments where you need to balance speed of iteration with long-term system health

Are a quick study—this team sits at the intersection of a large number of different complex technical systems that you'll need to understand (at a high level) to be effective

Strong Candidates May Also Have:

Experience building human data labelling interfaces, human-in-the-loop systems, or data collection pipelines

Familiarity with how preference data and reward models are used in AI model training

Experience working with researchers who are internal users/customers

Background in building, and improving the user-experience of user-facing applications, particularly those involving complex UI interactions or annotation workf