Machine Learning Engineer, Marketplace Optimization

📋 Job Overview

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.

🏢 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.

🎯 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.

✅ Key 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.

📌 Required Qualifications

• 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.

⭐ Desirable Experience

• 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.

🎁 Benefits

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).

🛂 Visa & Eligibility

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.