Engineering Manager Ii, Ranking and Recommendations, Grocery and Retail

Uber Uber · Consumer · New York, NY · Engineering

Engineering Manager for Uber's Grocery & Retail Consumer Ranking Team, focusing on leading the evolution of the online ranking architecture and high-throughput serving stack. The role involves building the engine for seamless shopping experiences, optimizing for latency, cost, and availability, and partnering with Data Science and ML Engineering to integrate state-of-the-art models into a production environment at scale. Responsibilities include leading backend and ML engineers, owning the roadmap, driving reliability and performance, evolving data flow, setting technical vision for ranking infra, and scaling experimentation capabilities.

What you'd actually do

  1. Lead the team of backend and ML engineers owning the online ranking serving stack and integrations powering key consumer surfaces.
  2. Own the Roadmap: Define, prioritize, and execute an ambitious product roadmap through strong people leadership—managing, coaching, and growing all team members.
  3. Drive Reliability & Performance: Lead high-impact initiatives to improve p99 latency, tail-latency mitigation, and infra-cost efficiency for high-QPS services.
  4. Evolve Data Flow: Oversee the development of real-time and batch pipelines, ensuring online/offline consistency for features and model inputs.
  5. Technical Vision: Lay out the multi-quarter roadmap for ranking infra, leveraging senior ICs to drive architectural migrations and capacity planning.

Skills

Required

  • software engineering
  • engineering management
  • distributed systems
  • API/service design
  • high-scale production serving
  • SLOs/SLAs
  • incident response
  • performance/latency optimization
  • delivering ML-backed services
  • partnering with DS/MLE on model requirements and evaluation
  • verbal and written communication
  • influence technical and product roadmaps

Nice to have

  • people leadership
  • hiring and developing high-performing teams
  • managing teams of 10+ engineers
  • large-scale tech company experience
  • integrating with large-scale ML platforms
  • model rollout patterns
  • making practical tradeoffs among latency, model quality, capacity, and maintainability
  • leading multi-quarter re-architecture projects
  • cross-functional negotiation

What the JD emphasized

  • high-traffic backend systems
  • high-scale production serving
  • SLOs/SLAs
  • incident response
  • performance/latency optimization
  • ML-backed services
  • model requirements and evaluation
  • large-scale ML platforms
  • model rollout patterns
  • multi-quarter re-architecture projects

Other signals

  • leading the evolution of Uber’s online ranking architecture
  • integrate state-of-the-art models (Deep Learning, Embeddings, and GenAI)
  • high-throughput serving stack
  • real-time features, caching strategies, and storage solutions
  • ruthlessly optimizing for p99 latency, resource costs, and 99.99% availability