Senior Product Manager — Grocery Fulfillment Optimization.

Uber Uber · Consumer · New York, NY +2 · Product

Senior Product Manager for Uber Grocery Fulfillment Optimization, owning systems and decision logic for last-mile order fulfillment. The role focuses on marketplace algorithms, real-world logistics, and customer trust, involving dynamic adaptation to supply, demand, and failure modes. It requires deep partnership with Data Science to design matching, batching, and recovery systems, focusing on reliability, cost-effectiveness, and customer/earner experience. The role also involves designing resilient fulfillment systems, translating operational complexity into product requirements, and shifting fulfillment from reactive to proactive.

What you'd actually do

  1. Own the decision logic behind grocery fulfillment: how orders are matched, batched, planned, recovered, and completed when real-world conditions change.
  2. Partner closely with Marketplace and Data Science to define matching, batching, and assignment logic that balances reliability, cost-per-trip, earner experience, and store readiness, making tradeoffs explicit and testable in code, not debated in meetings.
  3. Design resilient fulfillment systems that treat recovery and graceful degradation as core product surfaces. Build backup fulfillment paths, partial-fulfillment recovery, and risk-based reassignment that materially reduce cancellations, missed deliveries, and partial fulfillment.
  4. Translate operational complexity into product and modeling requirements, ensuring systems behave predictably across edge cases, degraded states, and at scale.
  5. Shift fulfillment from reactive to proactive — build systems that anticipate risk (scheduled orders, supply variability, store performance) and adapt before customers feel the impact.

Skills

Required

  • 7+ years of product management experience delivering successful, durable products
  • Strong technical and operational fluency
  • Experience building algorithmic or ML-adjacent products in close partnership with Data Science
  • Hands-on data fluency (SQL, dashboards, instrumentation)
  • Exceptional attention to detail
  • Customer obsession
  • A strong “driver” mindset
  • Sound judgment under ambiguity
  • Influence without authority
  • A consistently high bar
  • A never-ending desire to learn and grow

Nice to have

  • Direct grocery, last-mile, or supply chain fulfillment experience at scale
  • Ownership of decision systems
  • Experience launching products side-by-side with Data Science on optimization models
  • Experience building products from 0→1 in ambiguous problem spaces

What the JD emphasized

  • ownership and outcomes you can point to
  • built complex systems directly or worked close enough to real-world operations
  • Experience building algorithmic or ML-adjacent products
  • Hands-on data fluency
  • Exceptional attention to detail
  • ownership of decision systems

Other signals

  • marketplace algorithms
  • real-world logistics
  • customer trust
  • matching, batching, and recovery systems
  • optimization models
  • reliability, unit economics, and long-term platform credibility