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. 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. Talk to your recruiter for more information. 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. To learn more about our benefits, visit our careers page here . 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 range for this position within the United States, including Illinois and Colorado. $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. Notice to Applicants for Jobs Located in NYC or Remote Jobs Associated With Office in NYC Only We used 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 promotion process, we provided Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound from August 21, 2023, through December 21, 2023. We resumed using Covey Scout for Inbound again on June 29, 2024, and ceased using Covey Scout for Inbound on April 30, 2026. The Covey tool has been reviewed by an independent auditor. Results of the audit may be viewed here: getcovey.com . Beware of recruitment scams: DoorDash, Deliveroo, and Wolt will never ask you to pay money or share sensitive financial information during hiring — learn more about our legitimate recruiting process at Recruitment Scam Awareness - DoorDash .

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About the Team
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.
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’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.

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machine learningmlaillmpythongo

Platform: greenhouse
Effort: quick

Benefits

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Dental

Vision

401(K)

Paid Time Off

Parental Leave

Wellness