Machine Learning Engineer, Marketplace Optimization

About the Team The mission of the Marketplace Optimization team is to ensure we maintain a healthy Ads Marketplace across all our verticals for both search (query context) and discovery experiences while fulfilling the requirements of all players in this marketplace. Marketplace Optimization is a critical part of the Ads Delivery funnel with a broad charter responsible for Bidding, Auction Design, Budget Pacing, Forecasting, and Ads Experimentation. Our work directly shapes advertiser experience, consumer experience, and marketplace balance. We leverage artificial intelligence and advanced ML, deep learning techniques to power decision-making in real time — from optimizing ad auctions to generating the most efficient bids and pacing budgets dynamically. These models sit at the heart of DoorDash’s ad delivery and play a pivotal role in improving the efficiency, fairness, and scalability of our marketplace. The opportunity is massive as DoorDash expands into new verticals like Grocery and Retail while building unique innovative ad products to leverage the closed loop marketplace.  About the Role We’re looking for a Machine Learning Engineer to help design, build, optimize and scale large-scale ML systems within the Ads Delivery funnel. Design, build, and deploy ML models and pipelines for pacing, bidding, auction and targeting optimization. Collaborate with Data Science and Product teams to develop and evaluate new algorithms through rigorous experimentation. Improve and scale existing ML infrastructure and data pipelines in partnership with Platform and Infra teams. Write high-quality, maintainable code and participate in system design and peer reviews. Learn from senior engineers and contribute to technical discussions that shape the team’s roadmap. Partner with Data Science and Marketing to design and execute lift tests; collaborate with Platform teams on budget A/B testing and evaluation framework. This is a high-impact role for someone who enjoys combining economic intuition, large-scale ML modeling, and applied engineering to solve complex real-world optimization problems. You’re excited about this opportunity because you will… Own impactful ML systems: Build and improve models that directly have a large impact on top and bottom line financials. Drive experimentation: Rapidly test hypotheses via robust sequential experiments; measure and explain your models’ impact on marketplace KPIs Optimize at scale: Work with one of the largest delivery datasets, building optimization pipelines that consider budget, fairness, assignment rates, and more Collaborate cross-functionally: Partner with engineering, analytics, product, and operations to iterate quickly, moving models from prototype to production Shape the future: We're one of the fastest growing Ads platforms in the world and we're looking to take that even further! We’re excited about you because you have… B.S., M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or a related field. 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 Industry experience building or maintaining machine learning systems in production. Solid understanding of machine learning fundamentals, statistics, and data modeling.  Strong programming skills in Python, Java, or C++, and experience with ML frameworks such as TensorFlow, PyTorch, or XGBoost. Excellent communication and collaboration skills — comfortable working with cross-functional partners in Product, DS, and Engineering. Curiosity and a growth mindset — motivated to learn, iterate quickly, and take ownership of impactful projects. Familiarity with auction systems, bidding, forecasting, or budget optimization (or other experience in ads or marketplaces) is a plus. Familiarity with experimentation science, including experience designing lift tests; marketplace incrementality experience is a plus. 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