Software Engineer Ii, Fellow Development

Handshake Handshake · Enterprise · San Francisco, CA · Engineering

Software Engineer II on the Fellow Development team at Handshake AI. This role focuses on building the learning, feedback, and community systems that support AI trainers in their work. The engineer will own features end-to-end, from technical design to launch, and collaborate with product, design, and operations teams. The role involves building full-stack systems to facilitate how fellows learn, receive feedback, and improve their AI model training and evaluation processes. The company is experiencing rapid growth in the AI data business, supporting frontier AI labs.

What you'd actually do

  1. Build the full-stack systems that power how fellows learn, get feedback, and improve their work
  2. Own features end-to-end across frontend and backend, from scoping and technical design through launch and iteration
  3. Partner closely with product and design to translate ambiguous requirements into polished, reliable user experiences
  4. Contribute meaningfully to architecture discussions and help establish patterns the team can build on
  5. Write clean, well-tested, maintainable code and actively participate in code reviews

Skills

Required

  • 3–5 years of professional software engineering experience building full-stack applications
  • Strong proficiency in TypeScript across frontend and backend surfaces
  • Hands-on experience with modern web stacks (React, Next.js, GraphQL)
  • Solid command of relational databases (PostgreSQL) and thoughtful data modeling
  • Track record of owning features independently and driving them to completion
  • Strong communicator who collaborates well with product, design, and cross-functional partners
  • Comfortable navigating ambiguity and making sound tradeoffs in a fast-paced environment

Nice to have

  • Strong user-facing product instincts
  • Background in edtech, learning platforms, or tools that help people improve at something
  • Familiarity with workflow orchestration tools like Temporal or async job systems

What the JD emphasized

  • build the learning, feedback, and community systems that help Handshake AI’s network of AI trainers do their best work
  • own features end-to-end
  • ship tools that directly shape how tens of thousands of people train and evaluate AI models

Other signals

  • building systems that help AI trainers do their best work
  • tens of thousands of people train and evaluate AI models
  • data spend for AI training will increase by 3-5x in the next few years