Director of Engineering, Logistics

DoorDash DoorDash · Consumer · San Francisco, CA · 342 Logistics Engineering

Director of Engineering for DoorDash's Logistics Org, leading 100+ engineers across Backend, Mobile, ML, and Operations Research. The role focuses on architecting intelligent, real-time systems, optimization engines, and high-throughput AI platforms for fulfillment, dispatching, dynamic pricing, ETAs, and supply-demand balancing. Key responsibilities include defining AI/Platform vision, architecting real-time decision engines, building production-grade ML, scaling infrastructure, and cross-functional leadership.

What you'd actually do

  1. Define the AI & Platform Vision: Drive the engineering strategy, transitioning bespoke algorithmic solutions into a unified, scalable AI/Optimization platform.
  2. Architect Real-Time Decision Engines: Lead the development of systems that solve complex combinatorial problems (VRP, assignment, pricing) in milliseconds at massive scale.
  3. Build "Production-Grade" ML: Ensure that our machine learning models aren't just accurate in a notebook, but are backed by robust feature stores, low-latency serving infrastructure, and rigorous backtesting frameworks.
  4. Scale High-Throughput Infrastructure: Evolve the underlying distributed systems to handle exponential growth, ensuring the platform remains stable under the load of millions of concurrent requests.
  5. Cross-Functional Leadership: Partner with Business, Product, and Data Science to turn high-level marketplace objectives into technical roadmaps that balance long-term platform health with immediate business impact.

Skills

Required

  • Large-Scale Systems
  • Machine Learning
  • Mathematical Optimization
  • software engineering experience
  • leading engineering managers and multiple teams
  • building and scaling high-performing engineering organizations
  • strategic mindset
  • architectural decisions at scale
  • operate at the intersection of engineering, product, and business
  • talent development
  • building culture

Nice to have

  • Distributed Systems

What the JD emphasized

  • AI & Platform Vision
  • Real-Time Decision Engines
  • Production-Grade" ML
  • Scale High-Throughput Infrastructure
  • Applied AI

Other signals

  • architect the intelligent, real-time systems that comprise the "brain" of our fulfillment engine
  • builds the optimization engines and high-throughput AI platforms
  • foundational models and workflows
  • architect of our three-sided marketplace
  • core fulfillment platforms responsible for real-time dispatching, dynamic pricing task-allocation, ETAs and supply-demand balancing
  • marry complex algorithmic research with "five-nines" platform reliability
  • Define the AI & Platform Vision
  • Architect Real-Time Decision Engines
  • Build "Production-Grade" ML
  • Scale High-Throughput Infrastructure
  • Applied AI