As a Machine Learning Engineer on the Drive team at DoorDash, you will be responsible for developing and optimizing machine learning systems that enhance delivery and pickup time estimations, merchant prep-time predictions, and logistics decision-making. The team focuses on building AI-powered solutions that improve the efficiency and reliability of deliveries across diverse merchant behaviors. You will work closely with software engineers, product managers, and data scientists to bring innovative machine learning capabilities into production. This role offers the opportunity to tackle complex logistics challenges using a variety of machine learning techniques.

• 5+ years of industry experience building and shipping production machine learning systems with measurable business impact.
• Strong experience developing production machine learning models using modern deep learning frameworks such as PyTorch.
• Experience building, deploying, monitoring, and maintaining production ML systems end-to-end.
• Strong software engineering skills in Python and experience with modern ML infrastructure and tooling.
• Deep expertise in at least one of the following areas: Deep Learning, Reinforcement Learning, Optimization, Large Language Models (LLMs), or Vision-Language Models (VLMs).
• Experience applying machine learning to estimation, ranking, prediction, optimization, or decision-making problems at production scale.

• Build next-generation machine learning models for delivery ETA, pickup ETA, merchant prep-time estimation, and order release prediction.
• Develop deep learning models that leverage large-scale spatiotemporal, marketplace, and behavioral signals.
• Apply reinforcement learning and optimization techniques to improve logistics decision-making and marketplace efficiency.
• Build AI-native product experiences using large language models and vision-language models.
• Design and run rigorous online experiments, production monitoring, and model iteration.
• Partner closely with software engineers, product managers, data scientists, and platform teams to bring new machine learning capabilities into production.