AI Platform Engineer for Scalable Systems

United States Digital Space LLC; United States; Hybrid; USD 180,000 - 201,000; Full time

Job summary

Notion is seeking an AI Platform Engineer to join our team and help build shared foundations for shipping AI products quickly and safely. You’ll collaborate with product teams to enable fast feature releases, while maintaining reliability and cost discipline across the platform.

You will work on production-grade guardrails, observability, and integrations, enabling teams to deploy models and providers responsibly as notional AI capabilities evolve.

Qualifications

• 2–4 years of experience as a Software Engineer.
• Experience with applied AI product development (prompting, evals, model integrations, or quality measurement).
• You've built out and scaled data processing pipelines at scale with Apache Spark or Ray.
• You’ve past experience working full-stack in TypeScript and node.js ecosystem.
• You have experience building MLOps and ML serving infrastructure.

Responsibilities

• Own and scale systems and core AI platform primitives from prototyping to production.
• Partner with product teams to provide paved paths and production-ready guardrails for faster AI features.
• Collaborate across infrastructure, libraries, APIs and product integration points for easy adoption.
• Operate critical AI systems in production, using observability to debug failures, improve latency and cost.
• Help the company safely adopt new models and capabilities via versioning, rollouts, and quality gates.

Skills

Software engineering

Applied AI

Spark/Ray pipelines

TypeScript/Node.js

MLOps / ML serving

Job description

Notion is seeking an AI Platform Engineer to join our team and help build shared foundations for shipping AI products quickly and safely. You’ll collaborate with product teams to enable fast feature releases, while maintaining reliability and cost discipline across the platform.

You will work on production-grade guardrails, observability, and integrations, enabling teams to deploy models and providers responsibly as notional AI capabilities evolve.