AI Applications Engineer

About the role

We’re looking for an AI Applications Engineer to help drive Notion’s business transformation efforts. In this role, you’ll be a strategic partner to our internal stakeholders (primarily GTM, Finance and People teams) and deliver and scale creative AI-driven solutions to multi-faceted problems with measurable business impact. You’ll also build reusable components, evaluation patterns, and operational guardrails that make AI delivery repeatable across teams.

Responsibilities

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

Requirements

4-8 years of experience as a Software Engineer or Data Engineer (or equivalent), with a track record of building and operating production systems end-to-end across application code, data, and infrastructure. Domain experience partnering with GTM and Finance is a plus.

• Experience building AI-enabled applications in production (LLMs and/or classical ML), including prompt + tool orchestration, retrieval, evaluation, and iteration based on real-world feedback
• 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 (e.g., CRM, finance, ticketing, HRIS), with the ability to navigate messy systems and still deliver reliable outcomes

Skills

• Impact-driven approach to technology: You use technology to drive measurable user and business outcomes, not as an end in itself. You stay current with tools like Cursor, Claude Code, and other AI-assisted development environments, and you’re pragmatic about choosing what delivers the most value
• Thoughtful problem-solving: You start with a clear understanding of context, align technical and non-technical partners, and translate AI concepts into actionable business outcomes. You navigate ambiguity and decompose complex problems into clean solutions
• Empathetic communication and collaboration: You communicate nuanced ideas clearly and enjoy collaborating across engineering, data, and business teams
• Familiarity with security, privacy, and governance for AI (access controls, PII handling, vendor/tool risk, auditability)