Senior Engineering Manager, Agent Context

Asana•New York City, NY

21d•Hybrid

About The Position

We're looking for an Engineering Manager to lead the Agent Context team in NYC. This team owns how Asana's AI systems search, retrieve, and reason over the work graph. This includes the search infrastructure, dense embedding pipelines, ranking systems, and evaluation frameworks that determine whether every AI experience at Asana is trustworthy. The mission is to make retrieval comprehensive, reliable, and fast at enterprise scale, positioning Asana as the coordination and memory layer for the agentic enterprise. This is a high-leverage platform role, as nearly every AI product at Asana depends on the systems this team builds. The role involves managing a team of senior engineers in New York, collaborating daily with partner teams in San Francisco and Warsaw, and working alongside a dedicated Product Manager. This role is based in our New York office with an office-centric hybrid schedule. The standard in-office days are Monday, Tuesday, and Thursday; most Asanas have the option to work from home on Wednesdays.

Requirements

8+ years of software engineering experience with 3+ years managing engineers, including senior engineers, on infrastructure or ML systems teams.
Shipped and operated production search, retrieval, or ML-serving systems at meaningful scale.
Deep working knowledge of the modern retrieval stack: inverted indexes and BM25, vector search and embedding models, hybrid retrieval, chunking strategies, re-ranking.
Experience building or heavily using evaluation systems for ML/AI quality: golden datasets, recall/precision metrics, LLM-as-judge, online experimentation.
Technically credible enough to review a design doc for an embedding backfill or an OpenSearch mapping change and catch the problem the team missed.
Experience leading distributed teams across time zones and know that it runs on written communication.
Write clearly, decisively, and often.

Nice To Haves

You've hired, coached, grown, and when necessary exited engineers and your former reports would work for you again.
You can speak concretely about systems you've run: the index architecture, the embedding models, the latency budgets, the incidents, and what you'd do differently.
Strong opinions about when each retrieval method is worth its cost.
You should be able to argue both sides of "semantic search everywhere" and tell us where you actually land.
You believe unmeasured quality claims are noise.
Engineers should leave design reviews with you sharper than they arrived.
Experience with LLM-powered products, agent systems, or RAG pipelines in production is strongly preferred.
Experience scaling a platform team that serves internal customers is a plus.

Responsibilities

Own the technical direction and delivery of Asana's retrieval stack end to end: lexical and semantic search, dense embedding generation and backfill at scale, chunking and ranking strategies, and RAG comprehensiveness across the work graph.
Build and operate the evaluation infrastructure that makes retrieval quality measurable: recall/precision benchmarks, offline and online evals, and comparative testing across retrieval backends.
Drive the cost, performance, and quality tradeoffs that define this space.
Set and enforce the bar for how other teams at Asana integrate with retrieval.
Hire, grow, and retain a team of strong senior engineers in NYC, and lead effectively across three time zones with deliberate async communication practices.
Partner with your PM counterpart to translate a multi-year platform thesis into a sequenced roadmap, and represent the team's technical strategy to engineering and product leadership.