Senior Product Manager - Grocery Fulfillment

About the Role

Uber Grocery & Retail is scaling into a daily-use, trust-based delivery platform. Fulfillment Optimization is at the center of that ambition.

As Senior Product Manager, Grocery Fulfillment, you will own the systems and decision logic that determine whether an order is fulfilled at all - and how reliably, efficiently, and cost-effectively it happens when conditions are imperfect.

This role sits at the intersection of marketplace algorithms, real-world logistics, and customer trust. You will work deeply with Marketplace and Data Science to design solutions that dynamically adapt to supply, demand, store readiness, and inevitable failure modes - without degrading the customer or earner experience.

The work spans both physical-world execution (stores, couriers, timing, constraints) and abstract optimization (modeling, tradeoffs, objective functions). The outcome directly impacts reliability, unit economics, and long-term platform credibility.

1. Own the decision logic behind grocery fulfillment, shaping how orders are planned, matched, recovered, and completed when real-world conditions change.
2. Design resilient fulfillment systems that improve reliability and reduce cancellations, missed deliveries, and partial fulfillment - treating recovery and degradation as core product problems, not edge cases.
3. Partner closely with Marketplace and Data Science to define algorithmic approaches that balance reliability, cost efficiency, earner experience, and store readiness.
4. Translate operational complexity into product and modeling requirements, ensuring systems behave predictably across edge cases, degraded states, and at scale.
5. Help shift fulfillment from reactive to proactive, building systems that anticipate risk (e.g., scheduled orders, supply variability, store performance) and adapt before customers feel the impact.

1. 5+ years of product management experience delivering successful, durable products, with clear ownership and outcomes you can point to. Your fingerprints are all over what shipped, what scaled, and what materially improved the business.
2. Strong technical and operational fluency. You've either built complex systems directly or worked close enough to real-world operations, such as supply chain, warehousing, fulfillment centers, or logistics networks to understand how theory breaks in practice. You can reason comfortably from high-level system design down to edge cases, data flows, and how software behaves under real operational constraints and failure modes.
3. Experience building algorithmic or ML-adjacent products in close partnership with Data Science. You understand how objective functions, constraints, experimentation, and model tradeoffs translate into real marketplace and fulfillment outcomes.
4. Exceptional attention to detail. You obsess over how systems behave in the real world - across customers, earners, merchants, and operations - and you notice the gaps, edge cases, and failure modes others miss.
5. Customer obsession. You take on messy, high-stakes problems and find elegant, simple solutions so customers and partners don't have to carry platform complexity.
6. A strong "driver" mindset. You are biased toward action, a great collaborator, a master disambiguator and simplifier, and relentless about pushing work to clarity and delivery.
7. Sound judgment under ambiguity. You can reason through multi-sided tradeoffs, reliability, cost, customer experience, earner experience, and merchant outcomes, and make principled decisions with imperfect information.
8. A consistently high bar. For your own contributions and for the products you ship.
9. A never-ending desire to learn and grow. You seek feedback, adapt quickly, and continuously sharpen your craft.

1. Fulfillment and/or marketplace experience, especially in logistics, last-mile delivery, or multi-sided platforms.
2. Experience building products from 0→1 in ambiguous problem spaces.
3. Experience in grocery, retail, CPG, or e-commerce environments.
4. Experience launching products side-by-side with Data Science teams.