Engineering Manager, Search & Context Platform

Notion Notion · Enterprise · San Francisco, CA · Engineering

Engineering Manager for Notion's Search & Context Platform team, responsible for the infrastructure powering lexical and semantic retrieval, agent context and memories, and enterprise capabilities. The role involves setting technical direction, product managing platform scope, and balancing foundational investments with fast iteration to support Notion's AI agents and 100M+ users.

What you'd actually do

  1. Lead a high-performing platform team responsible for search infrastructure (lexical & semantic), indexing (including streaming & batch data pipelines), retrieval (lexical + semantic + hybrid), and the context/memory primitives that power Notion's agents.
  2. Set a clear roadmap that balances foundational platform investments (latency, cost, reliability, scalability, freshness, completeness, security, enterprise readiness) with fast iteration on indexing new entities/features and building new context capabilities for agents.
  3. Operate the platform with a high reliability bar: SLOs, deep observability, on-call health, early-warning signals, and prevention-first incident/post-mortem practices and drive measurable improvements to search and context quality, performance, and reliability for Notion's largest customers and o
  4. Partner and build closely with the Search & Context product team, the AI team, and other consumers of the platform on the right interfaces, capabilities, and commitments.
  5. Staying ahead of the needs of your customer teams by anticipating where the product is going, investing in capacity, capabilities, and primitives and making sure the platform accelerates product velocity.

Skills

Required

  • 4+ years of experience leading engineering teams with a track record of shipping high-quality systems in a fast-paced environment.
  • A technically leaning management style—you stay close to the code and the design, can credibly debate architecture and tradeoffs with senior ICs, and raise the technical bar of your team.
  • Sufficient depth in search, retrieval, or large-scale data/indexing systems: lexical search (e.g. BM25), semantic search (embeddings, ANN/vector indexes), big data pipelines, hybrid retrieval, ranking, and the surrounding infrastructure.
  • Experience product managing a platform or infrastructure scope: continuous evaluation technical architecture, SLAs, and a roadmap on behalf of internal customer teams, and making prioritization calls when those customers want different things.
  • Strong systems judgment around scalability, performance, security, build vs buy and enterprise readiness—you've shipped systems that had to be fast, cheap, secure, and reliable at scale, not just functional.
  • A bias for making hard tradeoffs to unblock product velocity while protecting the long-term health of the platform; comfortable saying "yes, with these constraints" or "no, here's the better path."
  • Experience working closely with product and AI teams as customers of a platform—you can speak both languages and translate between them.
  • High tolerance for ambiguity and rapid change; you enjoy operating in a space where both the product surface (agents, AI) and the underlying technology (retrieval, LLMs) are evolving quickly.

Nice to have

  • Experience building agentic or tool-using systems, or platforms that serve LLM-based products.
  • Familiarity with permissioned, multi-tenant enterprise data—ACL-aware indexing, retrieval, and audit.
  • Understanding of classic information retrieval metrics, ranking, or applied ML.
  • Has led teams through rapid scope and priority changes and evolving org boundaries.

What the JD emphasized

  • managing agent context and memories
  • scalability, performance, security, and enterprise capabilities
  • order of magnitude growth
  • support an order of magnitude growth—both in the volume of content we index and in the load that agents now place on the retrieval layer
  • foundational platform investments
  • fast iteration
  • high reliability bar
  • enterprise readiness
  • making hard tradeoffs to unblock product velocity

Other signals

  • powers Notion's agents
  • managing agent context and memories
  • scalability, performance, security, and enterprise capabilities
  • order of magnitude growth
  • support an order of magnitude growth—both in the volume of content we index and in the load that agents now place on the retrieval layer