Staff Machine Learning Engineer, Fulfillment Planning

Doordashusa; San Francisco (CA); Hybrid; USD 137,100 - 201,600; Full time

Benefits offered by this job

Comprehensive benefits package

401(k) plan with employer matching

Paid parental leave

Wellness benefits

Commuter benefits

Job summary

Doordashusa is seeking a Staff Machine Learning Engineer to lead the design, development, and deployment of large-scale production ML systems that enhance real-time decision-making across DoorDash's fulfillment ecosystem. This role will involve owning ML systems for logistics optimization and working closely with cross-functional teams to drive cost-effective solutions.

The ideal candidate will have over 8 years of experience in building and deploying ML systems and be fluent in Python. The position offers the opportunity to influence architectural frameworks and mentoring within a collaborative environment.

Qualifications

• 8+ years of industry experience building and deploying production-scale machine learning systems.
• Strong machine learning fundamentals and application to large-scale systems.
• Experience with modern ML frameworks, especially deep learning frameworks.

Responsibilities

• Lead design and deployment of large-scale production ML systems.
• Collaborate with cross-functional teams to improve delivery quality.
• Define architectures and modeling standards for efficient implementations.

Skills

Machine Learning

Python

Data Modeling

Distributed Systems

Optimization

Hands-on Architecture

Education

Bachelor's degree in a relevant field

Tools

Apache Cassandra

Redis

Kafka

Job description

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 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.

Compensation

The successful candidate's starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.

In addition to base salary, the compensation for this role includes opportunities for equity grants.

DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes 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 (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family‑forming assistance, and a mental health program, among others.

See below for paid time off details:

• For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year.
• For hourly roles: vacation accrued at about 1 hour for every 25.97 hours worked (e.g. about 6.7 hours/month if working 40 hours/week; about 3.4 hours/month if working 20 hours/week), and paid sick time accrued at 1 hour for every 30 hours worked (e.g. about 5.8 hours/month if working 40 hours/week; about 2.9 hours/month if working 20 hours/week).

The national base pay ranges for this position within the United States, including Illinois and Colorado.

• $137,100 — $201,600 USD
• $167,800 — $246,800 USD
• $203,500 — $299,300 USD

About DoorDash

At DoorDash, our mission to empower local economies shapes how our team members move quickly, learn, and reiterate in order to make impactful decisions that display empathy for our range of users—from Dashers to merchant partners to consumers.

We are a technology and logistics company that started by enabling door‑to‑door delivery, and we are looking for team members who can help us go from a company that is known as the place you order food to a company that people turn to for any and all goods.

DoorDash is growing rapidly and changing constantly, which gives our team members the opportunity to share their unique perspectives, solve new challenges, and own their careers.

We're committed to supporting employees’ happiness, healthiness, and overall well‑being by providing comprehensive benefits and perks including premium healthcare, wellness expense reimbursement, paid parental leave and more.

Our Commitment to Diversity and Inclusion

We’re committed to growing and empowering a more inclusive community within our company, industry, and cities. That’s why we hire and cultivate diverse teams of people from all backgrounds, experiences, and perspectives.

We believe that true innovation happens when everyone has room at the table and the tools, resources, and opportunity to excel.

Statement of Non-Discrimination: In keeping with our beliefs and goals, no employee or applicant will face discrimination or harassment based on: race, color, ancestry, national origin, religion, age, gender, marital/domestic partner status, sexual orientation, gender identity or expression, disability status, or veteran status. Above and beyond discrimination and harassment based on “protected categories,” we also strive to prevent other subtler forms of inappropriate behavior (i.e., stereotyping) from ever gaining a foothold in our office. Whether blatant or hidden, barriers to success have no place at DoorDash. We value a diverse workforce – people who identify as women, non-binary or gender non-conforming, LGBTQIA+, American Indian or Native Alaskan, Black or African American, Hispanic or Latinx, Native Hawaiian or Other Pacific Islander, differently-abled, caretakers and parents, and veterans are strongly encouraged to apply. Thank you to the Level Playing Field Institute for this statement of non-discrimination.

Pursuant to the San Francisco Fair Chance Ordinance, Los Angeles Fair Chance Initiative for Hiring Ordinance, and any other state or local hiring regulations, we will consider for employment any qualified applicant, including those with arrest and conviction records, in a manner consistent with the applicable regulation.

If you need any accommodations, please inform your recruiting contact upon initial connection.