Senior Engineering Manager (Agent Context)

Join Asana as a Senior Engineering Manager to lead the Agent Context team in NYC. This team is responsible for the search infrastructure, dense embedding pipelines, ranking systems, and evaluation frameworks that determine the trustworthiness of Asana's AI experiences. You will manage a team of senior engineers, collaborate with partner teams in San Francisco and Warsaw, and work alongside a dedicated Product Manager. Your mission is to make retrieval comprehensive, reliable, and fast at enterprise scale, positioning Asana as the coordination and memory layer for the agentic enterprise.

• Lead the Agent Context team in developing and enhancing Asana's AI systems for search, retrieval, and reasoning over the work graph.
• Own the technical direction and delivery of Asana's retrieval stack end to end, including lexical and semantic search, dense embedding generation, and ranking strategies.
• Manage a team of senior engineers, collaborating with partner teams across different locations, and represent the team's technical strategy to engineering and product leadership.
• 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
• You're technically credible enough to review a design doc for an embedding backfill or an OpenSearch mapping change and catch the problem the team missed. You don't need to write the code, but engineers should leave design reviews with you sharper than they arrived
• You have shipped and operated production search, retrieval, or ML-serving systems at meaningful scale. 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
• Deep working knowledge of the modern retrieval stack inverted indexes and BM25, vector search and embedding models, hybrid retrieval, chunking strategies, re-ranking and strong opinions about when each is worth its cost. You should be able to argue both sides of "semantic search everywhere" and tell us where you actually land
• You've led distributed teams across time zones and know that it runs on written communication. You write clearly, decisively, and often
• You've built or heavily used evaluation systems for ML/AI quality: golden datasets, recall/precision metrics, LLM-as-judge, online experimentation. You believe unmeasured quality claims are noise
• 8+ years of software engineering experience with 3+ years managing engineers, including senior engineers, on infrastructure or ML systems teams. You've hired, coached, grown, and when necessary exited engineers and your former reports would work for you again