Staff Machine Learning Engineer, Fulfillment Planning

About The Team

The Fulfillment Planning team builds the intelligence that powers DoorDash’s logistics network. We optimize how deliveries are planned and executed across the full delivery lifecycle, improving customer experience, merchant outcomes, Dasher efficiency, and DoorDash profitability.  Our mission is to improve fulfillment quality while reducing fulfillment cost. We do this by applying machine learning, optimization, and systems engineering to the core decisions behind assignment, routing, batching, timing, and fulfillment estimation.

The Team Works On Some Of Doordash’s Most Important Logistics Systems, Including

The core assignment engine that matches deliveries with Dashers in real time.

Real-time ETA and fulfillment estimation systems for consumers, Dashers, and merchants across diverse geographies and all business lines.

Assignment and planning algorithms for specialized delivery types, including grocery, retail, parcel, and catering.

ML models and optimization algorithms that shape demand, improve service quality, and reduce cost.

Tier-0 logistics services that require high reliability, low latency, and strong operational discipline.

The team also builds reusable ML systems and modeling patterns that scale across DoorDash’s logistics ecosystem. This role will help define the technical direction and best practices for logistics ML at DoorDash.

About The Role

We’re looking for a

Staff Machine Learning Engineer

to lead the design, development, and deployment of

large-scale production ML systems

that drive real-time decisioning across DoorDash’s fulfillment ecosystem.

You will start by owning ML systems for assignment and fulfillment estimation, partnering closely with Product, Data Science, Engineering, and Platform teams to improve delivery quality, cost, and efficiency. Over time, you may also contribute to adjacent areas such as batching, fulfillment execution, demand shaping, and logistics optimization across DoorDash’s business lines.

This is a high-impact individual contributor role for someone who enjoys building 0→1 ML systems, operating at Staff-level scope, and influencing technical direction across multiple teams. You will define architectures, set modeling and deployment standards, mentor other engineers, and help shape how DoorDash applies machine learning to logistics at scale.

You’re excited about this opportunity because you will…

Own and build

foundational ML systems

that directly impact delivery quality, cost, and overall logistics efficiency across DoorDash.

Work on

challenging, real-world machine learning problems

, including real-time assignment, routing, and fulfillment estimation.

Lead

0→1 ML initiatives

, defining how machine learning and optimization are applied across fulfillment products.

Influence architecture, strategy, and execution for a Tier-0 service critical to DoorDash’s logistics platform.

Collaborate closely with Product, Data Science, and Platform Engineering in a highly cross-functional environment.

Establish best practices for model development, deployment, monitoring, retraining, and governance.

Define and lead DoorDash’s cutting-edge AI vision for logistics: an LLM-inspired foundation model for intelligence across logistics

Mentor other engineers and raise the technical bar for logistics ML across the organization.

We’re excited about you because…

You have 8+ years of industry experience building and deploying production-scale machine learning systems.

You have strong machine learning fundamentals and know how to apply them to large-scale production systems.

You are fluent in Python

You have hands-on experience with modern ML frameworks, especially deep learning frameworks.

You have designed, launched, and operated mission-critical ML models or systems in production, including monitoring, retraining, reliability, and governance.

You can lead complex technical projects end to end and influence stakeholders across multiple teams or organizations.

You communicate clearly with both technical and non-technical audiences.

You are comfortable operating in ambiguous problem spaces and turning 0→1 ideas into production systems.

You have built or shipped large-scale ML models for recommendation, ads, marketplace, logistics, or other domains.

You have experience with knowledge distillation from large teacher models into efficient production models.

About The Team

The Storage teams build and operate online stateful systems and abstractions that are reliable, efficient, secure and easy to use for DoorDash Engineering. The teams are responsible for understanding Product Engineering’s evolving needs and developing platform and infrastructure capabilities to serve them. The team currently supports CockroachDB, Cassandra, Kafka and Redis as well as data abstraction services to reduce the complexity of interacting with storage systems for Product Engineers.

About The Role

We’re hiring a

Data Solutions Engineer

with deep expertise in distributed databases, particularly Apache Cassandra, Redis, Kafka, and database agnostic abstractions. In this role, you will design, optimize, and scale distributed data access layers that power DoorDash’s most critical systems, ensuring high availability, low latency, and fault tolerance.

You’ll serve as a hands-on architect and technical partner to product engineering and infrastructure teams, helping translate complex business requirements into resilient and scalable data models. Your work will directly influence the evolution of

Taulu

, DoorDash’s unified storage abstraction layer, by shaping best practices and identifying platform gaps through real world engagements.

This is a

high-impact, cross functional role

that combines deep technical expertise with a customer centric approach. You’ll lead solutioning engagements from design through production, drive the adoption of Taulu modeling best practices, and ensure that our systems meet goals around reliability, cost efficiency, and velocity. You must be located in San Francisco, Sunnyvale, Seattle or New York for this hybrid opportunity.

You’re excited about this opportunity because you will…

Design and implement

highly scalable, fault tolerant distributed database solutions using Taulu, Apache Cassandra, Redis, Kafka, and other paved path storage solutions.

Architect and optimize

multi-region, globally distributed systems to meet our high standards for availability, latency, and throughput.

Lead data modeling, performance tuning, and capacity planning

for large-scale, mission-critical storage workloads.

Partner with product engineering and infrastructure teams

to deeply understand domain specific data needs and guide them in adopting paved path storage solutions.

Serve as the DRI for solutioning engagements

, owning modeling in Taulu from experimentation through launch and scale.

Shape the evolution of Taulu

by identifying abstraction gaps and converting customer feedback into platform improvements.

Apply workload-aware design

patterns, including caching strategies, partitioning, and consistency tuning to improve performance and efficiency.

Drive adoption of operational best practices

across observability, schema design, capacity planning, and cost optimization across storage systems.

Promote clarity and continuity

by contributing to solutioning playbooks, decision logs, and architectural documentation.

We’re excited about you because…

You have 10+ years of experience designing and scaling distributed data systems, with deep expertise in NoSQL technologies like Apache Cassandra, DynamoDB, or ScyllaDB.

You have a strong command of distributed system concepts such as replication, partitioning, tunable consistency, and failure recovery.

You’ve led data modeling efforts for high-throughput, low-latency workloads and understand the real-world trade-offs involved in NoSQL schema design.

You are experienced with caching technologies like Redis or Memcached and know how to layer them effectively over storage systems to optimize for performance and cost.

You have a customer-first mindset, and thrive when working closely with product and platform teams to translate complex requirements into clean, scalable data models.

You are skilled at communicating complex architecture decisions and building alignment across infrastructure and product engineering organizations.

You have a track record of mentoring engineers, influencing data architecture at scale, and fostering best practices in reliability, observability, and data access patterns.

You document decisions, share learnings, and take pride in contributing to reusable playbooks and durable frameworks for others to build upon.

Bonus: You’ve worked on or contributed to open-source distributed databases.

Notice Regarding Use Of Ai And Automated Tools

To streamline our hiring process, DoorDash utilizes an automated recruitment tool called Gem.

How It Works

Gem assists our recruiting team by evaluating job related qualifications and characteristics in connection with hiring. The tool is designed and used to support - rather than replace - human decision-making; trained personnel make final decisions with meaningful human review and oversight, and DoorDash does not use Gem or other AI-enabled tool  in a manner that has the effect of subjecting applicants or employees to discrimination based on any protected characteristic or proxy or for engaging in any protected activity under applicable law.

Data Retention, Privacy & Bias Audit:

Data collected during this process is retained in accordance with our

Candidate Privacy Policy

and applicable state laws. In compliance with New York City Local Law 144, the independent bias audit summary for Gem is publicly available for review at our

Careers Page

.

Notice to Applicants for Jobs Located in NYC or Remote Jobs Associated With Office in NYC Only

We use Covey as part of our hiring and/or promotional process for jobs in NYC and certain features may qualify it as an AEDT in NYC. As part of the hiring and/or promot