Software Engineer, Human Data Interface

Anthropic Anthropic · AI Frontier · New York, NY +1 · AI Research & Engineering

Software Engineer role focused on building and architecting data collection pipelines and interfaces for human data vendors and crowdworkers to improve AI models. This involves full-stack development, user experience design for data collectors, and collaboration with researchers and data operations teams.

What you'd actually do

  1. Architect and build data collection pipelines that support rapid iteration, balancing data quality and system maintainability
  2. 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
  3. Collaborate closely with research teams to understand evolving data needs and iterate quickly on collection methods
  4. Partner with our Human Data Operations team to understand the end-to-end data workflow and design interfaces that make their jobs easier
  5. Prioritize and juggle multiple workstreams, making trade-off decisions in a fast-moving environment where research priorities can shift quickly

Skills

Required

  • full-stack engineering
  • building internal tools
  • understanding user needs
  • system design
  • collaboration

Nice to have

  • human data labelling interfaces
  • human-in-the-loop systems
  • data collection pipelines
  • preference data and reward models
  • user-facing application development
  • complex UI interactions
  • annotation workflows

What the JD emphasized

  • rapid iteration
  • high-quality data
  • evolving data needs
  • fast-moving environment
  • research priorities can shift quickly
  • broad experience across the stack
  • building internal tools
  • working with users of the tools
  • balance speed of iteration with long-term system health
  • complex technical systems
  • human data labelling interfaces
  • human-in-the-loop systems
  • data collection pipelines
  • preference data and reward models are used in AI model training
  • researchers who are internal users/customers
  • user-experience of user-facing applications
  • complex UI interactions or annotation workflows
  • system design
  • evolve gracefully as requirements change
  • influencing technical and product direction

Other signals

  • builds systems that collect data to improve models
  • designs systems that are performant at scale and resilient
  • works closely with researchers, data operations partners, and crowdworkers/vendors