The Machine Learning Engineer will join the Marketplace Optimization team at DoorDash, focusing on enhancing the Ads Delivery funnel through the design, optimization, and scaling of large-scale machine learning systems. This role involves leveraging AI and advanced machine learning techniques to improve ad auctions, bidding, and budget pacing. The engineer will collaborate with data science and product teams to develop new algorithms and improve existing infrastructure. This position is crucial for driving impactful changes in the Ads Marketplace as DoorDash expands into new verticals.

• B.S., M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or a related field.
• Proficiency in using AI coding tools in the full software development lifecycle.
• 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.
• Curiosity and a growth mindset, motivated to learn and take ownership of projects.
• Familiarity with auction systems, bidding, forecasting, or budget optimization is a plus.
• Familiarity with experimentation science, including experience designing lift tests, is a plus.

• 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.
• Partner with Data Science and Marketing to design and execute lift tests.
• Collaborate with Platform teams on budget A/B testing and evaluation framework.