Software Engineer, Human Data Interface

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 workflows

• Strong instincts around system design — building things that evolve gracefully as requirements change

• Experience influencing technical and product direction on a team