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