Staff Machine Learning Engineer - DashPass
Doordashusa
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Benefits offered by this job
Job summary
Doordashusa is looking for a Staff Machine Learning Engineer in San Francisco to develop large-scale ML systems aimed at personalizing the DashPass Subscriber experience. With over 8 years of experience and proficiency in Python, Java, and C++, the ideal candidate will design and implement innovative solutions impactfully.
This role combines technical expertise with mentorship opportunities, aiming to optimize customer growth through cutting-edge ML techniques while contributing significantly to team development.
Qualifications
- 8+ years of industry experience building production-scale ML systems.
- Proficiency in using AI coding tools in the full software development lifecycle.
- Strong understanding of probability theory, statistics, and machine learning fundamentals.
Responsibilities
- Drive the design and development of large-scale ML/optimization systems.
- Partner closely with cross-functional teams to design experiments and model frameworks.
- Build and deploy 0→1 ML systems to improve subscriber outcomes.
Skills
Education
Tools
Job description
DashPass is DoorDash’s subscription loyalty program that delivers lower delivery fees and a host of additional benefits and value to a large subscriber base of both paid and sponsored subscriptions. DashPass subscribers enjoy lower delivery fees, faster ETAs, 3rd party partnerships, and special discounts and promotions to get maximum value of their membership.
Several teams are part of the DashPass org including Growth, Habituation, Member Experience, Exclusive Offers, and Partnerships. All of these teams require personalized targeting of offers and promotions to entice active Doordash consumers to sign up for DashPass and actively engage with their subscription in a personalized way, as well as reduce churn by offering personalized incentives to DashPass subscribers to keep using their subscription.
We are forming a new team that will leverage AI and advanced ML to power decision making in real-time – from personalized sign up promotions to progressive reward systems, and to pre‑cancel offers that retain subscribers.
DashPass is continuously building new benefits and offerings to drive more value for our subscribers, and personalization is our next big bet to help efficiently grow our subscriber base to 2030 and beyond.
We’re looking for a Staff Machine Learning Engineer to drive the design and development of large-scale ML/optimization systems to target personalization efforts across the DashPass Subscriber journey.
- Contribute to Causal inference modeling to measure the incremental impact of DashPass Subscriber acquisition and retention strategies.
- Incentive optimization frameworks that personalize progressive rewards to improve spend efficiency.
- Budget allocation and forecasting models that identify optimal spend across acquisition, referrals, and retention.
- Partner closely with Product, Data Science, and Engineering teams to design experiments, model frameworks, and production ML systems that directly impact DashPass subscriber growth metrics.
- Provide technical mentorship and guidance to engineers and cross‑functional partners — leading through influence, not management.
- Build and deploy 0→1 ML systems that improve subscriber outcomes and marketplace health.
- Set best practices for model training, evaluation, deployment, and monitoring
This is a highly impactful IC role for someone who enjoys combining economic intuition, large-scale ML modeling, and system design to solve complex real‑world optimization problems.
- M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or a related field.
- 8+ years of industry experience building production‑scale ML systems.
- Proficiency in using AI coding tools (e.g., Claude Code, Codex, Cursor) in the full software development lifecycle, including designing, generating code, testing, monitoring and releasing software.
- Strong understanding of probability theory, statistics, and machine learning fundamentals.
- Strong programming skills in Python, Java, or C++, and experience with ML frameworks such as TensorFlow, PyTorch, or XGBoost.
- Interest in building and leading a new team that has broad impact across a wide range of problem spaces to support a critical business line.
- Proven ability to lead cross‑functional initiatives and drive complex technical projects end‑to‑end.
- Excellent communication skills — able to explain technical concepts to product, business, and engineering audiences.
- Experience in subscriptions growth or marketplace systems is a plus.
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.
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.
- 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.
- 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.
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 offers 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.
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).
National base pay ranges for this position within the United States, including Illinois and Colorado.
Salary ranges: $137,100 – $201,600 USD, $167,800 – $246,800 USD, $203,500 – $299,300 USD.
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.
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.
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.
We will consider for employment any qualified applicant, including those with arrest and conviction records, in a manner consistent with the applicable regulation.
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