Staff Software Engineer, Machine Learning - Personalization

About the Machine Learning Engineer Come help us build the world's most reliable on-demand, logistics engine for last-mile retail delivery! We're looking for an experienced machine learning engineer to help us develop modern growth and personalization models that power DoorDash's growing retail and grocery business. About the Role We’re looking for a passionate Applied Machine Learning expert to join our team. As a Staff Machine Learning Engineer, you’ll be conceptualizing, designing, implementing, and validating algorithmic improvements to the growth and personalization experiences at the heart of our fast‑growing grocery and retail delivery business. You will use our robust data and machine learning infrastructure to implement new ML solutions to make the consumer search experience more relevant, seamless, and delightful across grocery, convenience, and many other retail categories. You will demonstrate a strong command of production level machine learning, experience with solving end‑user problems, and collaborate well with multi‑disciplinary teams. You will report into the engineering manager on our Personalization team. We expect this role to be hybrid with some time in‑office and some time remote (#LI-Hybrid). You’re excited about this opportunity because you will… Develop production machine learning solutions to build a world class personalized shopping experience for a diverse and expanding retail space Partner with engineering and product leaders to help shape the product roadmap applying ML Mentor junior team members, and lead cross functional pods to create collective impact We’re excited about you because you have… 8+ years of industry experience developing machine learning models with business impact, and shipping ML solutions to production. 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 M.S., or PhD. in Statistics, Computer Science, Math, Operations Research, Physics, Economics, or other quantitative field Expertise in applied ML for causal inference and recommendation systems – both classical and deep learning based. Additional familiarity with explore/exploit/MAB algorithms & LLMs is a plus. Machine learning background in Python; experience with PyTorch or TensorFlow preferred. Ability to communicate technical details to nontechnical stakeholders You keep the mission in mind, take ideas and help them grow using data and rigorous testing, show evidence of progress and then double down Desire for impact with a growth-minded and collaborative mindset About the Data Solutions Engineer 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. 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 the 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. 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). 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. #J-18808-Ljbffr Doordashusa