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 Door Dash’s ad delivery and play a pivotal role in improving the efficiency, fairness, and scalability of our marketplace. The opportunity is massive as Door Dash expands into new verticals like Grocery and Retail while building unique innovative ad products to leverage the closed‑loop marketplace.

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.

• 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!

• 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 Tensor Flow, 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.

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…