DoorDash USA
Machine Learning Engineer, Drive
About this role
DoorDash is hiring a Machine Learning Engineer for the Drive team to build prediction and intelligence systems powering merchant-initiated deliveries. You'll develop end-to-end ML solutions spanning delivery ETA estimation, reinforcement learning for logistics optimization, and multimodal AI applications, working across deep learning, optimization, and LLM/VLM technologies.
What you'll do
- Build and optimize machine learning models for delivery ETA, pickup ETA, merchant prep-time estimation, and order release prediction
- Develop deep learning models leveraging spatiotemporal, marketplace, and behavioral signals at production scale
- Apply reinforcement learning and optimization techniques to improve logistics decision-making and marketplace efficiency
- Create AI-native product experiences using LLMs and vision-language models to transform images into quality signals
- Design and execute rigorous online experiments and production monitoring to drive continuous model improvement
- Partner with engineers, product managers, and data scientists to deploy ML capabilities at scale
What they're looking for
- Production machine learning systems (end-to-end development, deployment, monitoring)
- Deep learning frameworks (PyTorch)
- Distributed data processing (Spark, Airflow)
- Python and software engineering best practices
- Deep learning, reinforcement learning, optimization, or LLM/VLM expertise
- Large language models and vision-language models
- Estimation, ranking, prediction, or optimization problem solving
- AI-assisted development tools (Claude Code, Codex, Cursor)
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DoorDash USA
DoorDash USA is building autonomous delivery systems including drones and robots, along with internal infrastructure platforms to support large-scale operations. The company is hiring robotics engineers, autonomous systems specialists, infrastructure engineers, and platform software engineers to develop flight control systems, mapping and localization capabilities, and distributed computing platforms.
View all jobs at DoorDash USALikely interview questions
- Walk us through an end-to-end ML system you built and shipped to production—what were the key challenges in moving from model development to deployment at scale?
- How have you approached building ML models that generalize across diverse user behaviors or operational workflows?