Staff Machine Learning Engineer, Fulfillment Planning

About the role

The Fulfillment Planning team builds the intelligence that powers DoorDash’s logistics network. They optimize delivery assignments, routing, batching, timing, and fulfillment estimation to improve customer experience, merchant outcomes, Dasher efficiency, and DoorDash profitability.

Responsibilities

• Owning and building foundational ML systems that directly impact delivery quality, cost, and overall logistics efficiency across DoorDash.
• Working on challenging, real-world machine learning problems, including real-time assignment, routing, and fulfillment estimation.
• Leading 0→1 ML initiatives, defining how machine learning and optimization are applied across fulfillment products.
• Influencing architecture, strategy, and execution for a Tier-0 service critical to DoorDash’s logistics platform.
• Collaborating closely with Product, Data Science, and Platform Engineering in a highly cross-functional environment.
• Establishing best practices for model development, deployment, monitoring, retraining, and governance.
• Defining and leading DoorDash’s cutting-edge AI vision for logistics: an LLM-inspired foundation model for intelligence across logistics.
• Mentoring other engineers and raising the technical bar for logistics ML across the organization.

Requirements

• 8+ years of industry experience building and deploying production-scale machine learning systems.
• Strong machine learning fundamentals and experience applying them to large-scale production systems.
• Fluent in Python.
• Hands-on experience with modern ML frameworks, especially deep learning frameworks.
• Experience with designing, launching, and operating mission-critical ML models or systems in production, including monitoring, retraining, reliability, and governance.
• Ability to lead complex technical projects end to end and influence stakeholders across multiple teams or organizations.
• Clear communication skills with both technical and non-technical audiences.
• Comfortable operating in ambiguous problem spaces and turning 0→1 ideas into production systems.
• Experience with large-scale ML models for recommendation, ads, marketplace, logistics, or other domains.
• Experience with knowledge distillation from large teacher models into efficient production models.

Qualifications

• Deep expertise in distributed databases, particularly Apache Cassandra, Redis, Kafka, and database agnostic abstractions.
• Strong command of distributed system concepts such as replication, partitioning, tunable consistency, and failure recovery.
• Experience with caching technologies like Redis or Memcached and knowledge of how to layer them effectively over storage systems to optimize for performance and cost.
• A customer-first mindset and ability to work closely with product and platform teams to translate complex requirements into clean, scalable data models.
• Skilled at communicating complex architecture decisions and building alignment across infrastructure and product engineering organizations.
• Track record of mentoring engineers, influencing data architecture at scale, and fostering best practices in reliability, observability, and data access patterns.
• Documenting decisions, sharing learnings, and contributing to reusable playbooks and durable frameworks for others to build upon.
• Bonus: Experience with open-source distributed databases.

Benefits

Comprehensive benefits package including a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws, medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program.