Backend Engineer, Forward Deployed Engineering

Stripe Stripe · Fintech · Canada · 8412 Billing Products

This role is on a Forward Deployed Engineering team that uses AI agents to solve complex enterprise user problems and turn those solutions into scalable platform capabilities. The engineer will work alongside AI agents, engage directly with users, build reusable solutions across product boundaries, and provide architectural guidance. The role focuses on applying AI to enhance engineering functions for large enterprise clients in the fintech space.

What you'd actually do

  1. Work alongside AI agents to serve users at scale.
  2. Engage directly with users.
  3. Build across product boundaries.
  4. Build reusable solutions, not one-off fixes.
  5. Provide architectural guidance.

Skills

Required

  • 5+ years of experience in software engineering, with a strong focus on backend systems.
  • Proven ability to design, build, and maintain highly available, scalable, and secure systems.
  • Strong command of distributed systems, API design, and data modeling.
  • Excellent problem-solving skills and the ability to quickly grasp complex technical and business domains.
  • Clear communicator, both written and verbal, with technical and non-technical stakeholders including external users.
  • Track record of working well in collaborative environments with product managers, TPMs, and other engineers.
  • Willingness to engage directly with users to understand requirements.

What the JD emphasized

  • AI handles the context-heavy, structured work
  • human FDEs focus on the parts that require engineering judgment, product thinking, and direct user relationships
  • building something different: an agent-augmented model
  • AI agents maintain real-time integration maps, run shadow tests against user setups, and perform automated state reconciliation between Stripe and user systems
  • work that requires an engineer: making judgment calls on ambiguous problems, building relationships with user engineering teams, making product decisions, and designing solutions
  • help shape the tooling itself, identifying what should be automated and what needs to stay human
  • working at the frontier of agent-augmented engineering, not just using AI tools but helping define how an engineering function operates alongside them

Other signals

  • AI agents handle context-heavy, structured work
  • turn solutions into platform capabilities
  • build engineering muscle that lets us serve every strategic user through the platform