Principal Machine Learning Engineer, Ads & Promos DeliverySunnyvale, CA

About the TeamThe Ads & Promos Delivery team powers the last-mile delivery of ads and promotions, two marketing products offered to merchants, connecting merchant intent with consumer demand across search and discovery experiences. As a Principal Engineer, you will lead the technical direction for AI-first experiences, including ranking and relevance systems that sit at the core of our ads marketplace and shape how ads are selected, ordered, and personalized in real time across all verticals.

You will design and build next-generation AI-first ranking systems using state-of-the-art techniques such as sequence modeling, deep learning, and large language models (LLMs). Your work will span query understanding, user and merchant representation learning, contextual relevance, and multi-objective optimization, balancing advertiser value, consumer experience, and marketplace health at scale.

You will set the long-term technical vision, drive cross-team alignment, and translate cutting-edge research into production systems that operate under strict latency, scale, and reliability constraints.

As DoorDash expands into 40

• global markets and new verticals such as Grocery and Retail, this role offers a rare opportunity to define how modern AI, including sequential models and LLM-powered decisioning, reshapes ads relevance in a closed-loop marketplace.

About the RoleApply state-of-the-art machine learning and LLM techniques to problems across personalization, query understanding, user and content understanding.

Rigorously evaluate ML and LLM models using a combination of offline analysis and online experimentation, designing metrics and experiments that clearly measure quality, impact, and tradeoffs.

Own the full model lifecycle from research to production, including data analysis, model development, evaluation, offline and online A/B testing, and continuous iteration.

Partner closely with product managers, data scientists, and designers to ensure AI-driven systems deliver meaningful, user-facing improvements.

Stay at the forefront of ML and AI innovation by assessing emerging research and translating promising approaches into scalable, production-ready systems.

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 willOwn impactful ML systems: Build and improve models that directly have a large impact on top and bottom line financials.

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 have5

• years of experience building, deploying, and scaling ML and AI models for large-scale, user-facing or data-intensive products.

BS, MS, or PhD in Computer Science, Engineering, or a related field, or equivalent practical experience.

Deep expertise in one or more of the following areas: deep learning, large language models, information retrieval, ranking and relevance, recommendation systems, natural language processing, or content understanding.

Strong programming skills in Python, Java, or C

•

• , with hands-on experience using ML frameworks such as PyTorch, TensorFlow, or XGBoost.

Extensive experience across the full ML lifecycle, including data analysis, feature engineering, iterative model development, rigorous offline and online evaluation, and ongoing monitoring and improvement.

Strong collaborator and communicator who thrives in fast-paced, cross-functional environments.

Product-minded and impact-driven, with a passion for applying cutting-edge ML and AI techniques to real-world problems.

Bonus Points ForExperience designing and deploying LLM-based systems, including prompt engineering and retrieval-augmented generation (RAG) architectures, Generative RecSys.

Experience solving large-scale, user-centric and content-centric personalization problems, including user modeling, retrieval, ranking, and relevance.

Demonstrated contributions to the ML community through open-source projects, publications, or applied research in areas such as ML, NLP, information retrieval, or related fields.