The ad says“Understanding of classic information retrieval metrics, ranking, or applied ML”
The ad says“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”
The ad says“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”
The ad says“Understanding of classic information retrieval metrics, ranking, or applied ML”
The ad says“4+ years of experience leading engineering teams with a track record of shipping high-quality systems in a fast-paced environment”
The ad says“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”
The ad says“Experience building agentic or tool-using systems, or platforms that serve LLM-based products”
The ad says“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”
The ad says“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”
The ad says“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”
Notion’s Search & Context Platform is the substrate that powers how 100M+ users and, increasingly, Notion’s agents find and reason over the right information
This team owns the search infrastructure and indexing systems powering lexical and semantic retrieval, the platform primitives for managing agent context and memories, and the scalability, performance, security, and enterprise capabilities that make all of it production-grade
Data growth is faster than ever, and the systems that power this need to rapidly evolve to 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
As the Engineering Manager for this team, you’ll lead a technically deep group of engineers building platform systems used by multiple product teams
Your most important customers are the Search & Context product team and the AI team (building agents on top of these primitives)
You’ll set the technical direction, product manage the platform scope on behalf of those customers, and make hard tradeoffs to move fast in service of product velocity — while keeping the platform sustainable, reliable, and secure
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
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
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
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
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
Build and lead the team through hiring, coaching, feedback, growth, and creating an environment where strong technical ICs do their best work
Contribute to Notion’s broader engineering practices around platform design, reliability, on-call, and AI-era infrastructure
Preferred experience
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
Experience working closely with product and AI teams as customers of a platform—you can speak both languages and translate between them
4+ years of experience leading engineering teams with a track record of shipping high-quality systems in a fast-paced environment
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
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
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
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
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 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
