Machine Learning Engineer, Marketplace Optimization

DoorDash USA•Sunnyvale, CA

About The Position

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. 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.

Requirements

B.S., M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or a related field.
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.

Nice To Haves

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.

Responsibilities

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.

Benefits

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)
medical, dental, and vision benefits
11 paid holidays
disability and basic life insurance
family-forming assistance
a mental health program
flexible paid time off/vacation, plus 80 hours of paid sick time per year
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)