Machine Learning Engineer, Drive

Doordashusa; San Francisco (CA); On-site; USD 150,000 - 200,000; Full time

Job summary

DoorDash is looking for a Machine Learning Engineer to join the Drive team in San Francisco. You will own ML systems end-to-end—from feature engineering and model development to experimentation, deployment, monitoring, and continuous iteration.

Work spans high‑impact areas like ETA prediction, prep‑time estimation, order release optimization, and AI‑native product experiences using LLMs and VLMs to improve merchant, consumer, and dasher outcomes.

Job description

About the Team

DoorDash Drive powers deliveries placed through merchants' own channels—including their websites, mobile apps, and phone orders—using DoorDash's logistics network. The Drive Machine Learning team builds the prediction and intelligence systems that power this business, including delivery and pickup time estimation, merchant prep-time prediction, order release optimization, logistics decision‑making, and AI‑powered delivery quality signals.

Drive presents a unique machine learning challenge. Every merchant has different operational workflows, preparation patterns, and customer expectations, requiring models that generalize across millions of deliveries while adapting to highly diverse merchant behavior. Our team has significant opportunities to improve prediction accuracy, optimize logistics decisions, and build AI‑native experiences that directly improve merchant, consumer, and dasher outcomes.

About the Role

As a Machine Learning Engineer on the Drive team, you'll own machine learning systems end-to-end—from feature engineering and model development to experimentation, deployment, monitoring, and continuous iteration.

Your work will span several high-impact problem areas:

• Build next-generation machine learning models for delivery ETA, pickup ETA, merchant prep‑time estimation, and order release prediction that improve reliability for merchants and consumers.
• Develop deep learning models that leverage large‑scale spatiotemporal, marketplace, and behavioral signals to improve prediction accuracy.
• Apply reinforcement learning and optimization techniques to improve logistics decision‑making, assignment strategies, and marketplace efficiency.
• Build AI‑native product experiences using large language models (LLMs) and vision‑language models (VLMs). For example, transform pickup photos, item verification flows, receipts,