AI Applications Engineer

Notion Notion · Enterprise · San Francisco, CA · Engineering

AI Applications Engineer at Notion to drive business transformation by delivering and scaling creative AI-driven solutions for internal stakeholders (GTM, Finance, People). The role involves building end-to-end AI solutions, establishing evaluation and production-readiness patterns, and creating reusable components and playbooks to enable safe and repeatable AI delivery across teams.

What you'd actually do

  1. Work with stakeholders to discover opportunities from ambiguous problem statements, translate them into scoped solutions, and drive iterative releases from idea to adoption
  2. Build and ship end‑to‑end AI solutions—from problem framing through data readiness, modeling, evaluation, and production rollout
  3. Establish evaluation and production-readiness patterns (metrics, monitoring, human-in-the-loop, rollout plans) so solutions are reliable at scale
  4. Create reusable components, tooling, templates, and playbooks that accelerate future projects and enable other teams to ship safely

Skills

Required

  • 4-8 years of experience as a Software Engineer or Data Engineer (or equivalent)
  • Experience building AI-enabled applications in production (LLMs and/or classical ML)
  • Strong production-readiness instincts, including observability, monitoring, quality gates, incident response, and safe rollouts/rollbacks in live business workflows
  • Systems and integration fluency across APIs, data pipelines, and enterprise tools
  • Impact-driven approach to technology
  • Thoughtful problem-solving
  • Empathetic communication and collaboration
  • Familiarity with security, privacy, and governance for AI (access controls, PII handling, vendor/tool risk, auditability)

Nice to have

  • Domain experience partnering with GTM and Finance is a plus

What the JD emphasized

  • track record of building and operating production systems end-to-end
  • Experience building AI-enabled applications in production
  • Strong production-readiness instincts
  • Systems and integration fluency
  • Impact-driven approach to technology
  • Thoughtful problem-solving
  • Empathetic communication and collaboration
  • Familiarity with security, privacy, and governance for AI

Other signals

  • build and ship end-to-end AI solutions
  • establish evaluation and production-readiness patterns
  • create reusable components, tooling, templates, and playbooks
  • experience building AI-enabled applications in production (LLMs and/or classical ML)
  • strong production-readiness instincts