About this role
About the Team
DoorDash is seeking a seasoned Engineering Director to lead our Logistics Org. You’ll lead a multi-disciplinary organization of 100+ engineers (Backend, Mobile, Machine Learning, and Operations Research) to architect the intelligent, real-time systems that comprise the "brain" of our fulfillment engine. This organization builds the optimization engines and high-throughput AI platforms that guide millions of Dashers and deploy tens of millions of dollars in real-time. This team sits at the heart of DoorDash’s fulfillment ecosystem, powering the delivery and courier data that drives our logistics engine. We build the foundational models and workflows that enable our customer teams (Order, Shopping, and beyond) to build extensible solutions on top of the platform.
About the Role
DoorDash is seeking a Director of Engineering with a deep background in Large-Scale Systems, Machine Learning, and Mathematical Optimization to lead our Logistics team in San Francisco. In this role, you are the architect of our three-sided marketplace. You will oversee the development of the core fulfillment platforms responsible for real-time dispatching, dynamic pricing task-allocation, ETAs and supply-demand balancing. This is a unique opportunity to marry complex algorithmic research with "five-nines" platform reliability. We are also looking for a leader who is deeply bought into the transformative potential of AI; someone who sees large language models, foundation models, and world model architectures not as adjacent curiosities but as core primitives for reimagining how logistics systems learn, plan, and adapt at scale.
You’re excited about this opportunity because you will…
• Define the AI & Platform Vision: Drive the engineering strategy, transitioning bespoke algorithmic solutions into a unified, scalable AI/Optimization platform.
• Architect Real-Time Decision Engines: Lead the development of systems that solve complex combinatorial problems (VRP, assignment, pricing) in milliseconds at massive scale.
• 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.
• 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.
• 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.
• Champion Engineering Excellence: Scale the organization by mentoring high-growth leaders and attracting world-class talent in the fields of Distributed Systems and Applied AI.
We’re excited about you because you have…
• A B.S., M.S., or Ph.D. in Computer Science or equivalent.
• An AI-native leadership