Asana8 дней назад
Senior Engineering Manager, Agent Context
Зарплата не указана
New York City
Обязанности
- 01Own 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
- 02Build and operate the evaluation infrastructure that makes retrieval quality measurable: recall/precision benchmarks, offline and online evals, comparative testing across retrieval backends
- 03Drive the cost, performance, and quality tradeoffs: determine when semantic search earns its infrastructure cost over lexical, hit latency targets without sacrificing recall, and ensure retrieval improvements compound into cheaper, faster downstream LLM calls
- 04Set and enforce the bar for how other teams at Asana integrate with retrieval: clear ownership of embedding decisions, rollout guidance, metrics to watch, and a platform posture that rejects unjustified infrastructure spend
- 05Hire, grow, and retain a team of strong senior engineers in NYC, and lead effectively across three time zones with deliberate async communication practices
- 06Partner 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
- 07Manage a team of senior engineers in New York collaborating daily with partner teams in San Francisco and Warsaw, and work alongside a dedicated Product Manager as your direct counterpart
Требования
- 018+ years of software engineering experience with 3+ years managing engineers, including senior engineers, on infrastructure or ML systems teams
- 02Proven track record of hiring, coaching, growing, and when necessary exiting engineers, with former reports eager to work for you again
- 03Experience shipping and operating production search, retrieval, or ML-serving systems at meaningful scale
- 04Ability to speak concretely about systems you've run: index architecture, embedding models, latency budgets, incidents, and lessons learned
- 05Deep 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
- 06Capacity to argue both sides of "semantic search everywhere" and articulate your own position
- 07Experience building or heavily using evaluation systems for ML/AI quality: golden datasets, recall/precision metrics, LLM-as-judge, online experimentation
- 08Belief that unmeasured quality claims are noise
- 09Technical credibility to review design docs for embedding backfills or OpenSearch mapping changes and catch issues the team missed
- 10Skill to elevate design reviews so engineers leave sharper than they arrived, without needing to write the code yourself
- 11Experience leading distributed teams across time zones, relying on written communication; you write clearly, decisively, and often
- 12Experience with LLM-powered products, agent systems, or RAG pipelines in production is strongly preferred
- 13Experience scaling a platform team that serves internal customers is a plus
Условия
- 01Estimated base salary range: $264,000 - $300,000
- 02Office-centric hybrid schedule: standard in-office days Monday, Tuesday, Thursday; option to work from home Wednesdays
- 03Based in Asana's New York office
- 04Benefits include mental health, wellness & fitness benefits; career coaching & support; inclusive family building benefits; long-term savings or retirement plans; in-office culinary options
- 05Compensation package may include equity and additional benefits