ML Engineer, End-to-End Logistics & AI Systems

DoorDash; San Francisco (CA); On-site; USD 150,000 - 270,000; Full time

Benefits offered by this job

401(k) with employer matching

16 weeks paid parental leave

Medical, dental, and vision benefits

Paid time off & paid sick leave

Disability and life insurance

Mental health program

Wellness benefits

Commuter benefits match

Job summary

DoorDash is seeking a Machine Learning Engineer to own end-to-end ML systems on the Drive team, from feature engineering to deployment and monitoring. You will work across ETA prediction, optimization, and AI-native experiences using LLMs and VLMs to improve logistics outcomes for merchants and dashers.

You will collaborate with software engineers, PMs, and data scientists to scale models, run experiments, and maintain production readiness in a fast-paced, data-driven environment.

Qualifications

• 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 and distributed data processing technologies such as Spark and Airflow.
• 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.
• Hands-on experience with LLMs or VLMs is a strong plus.
• Experience in logistics, marketplaces, or delivery platforms is helpful but not required.
• Proficiency using AI-assisted development tools (e.g. Claude Code, Codex, Cursor) throughout the software development lifecycle.
• You are located or are planning to relocate to San Francisco, CA, Sunnyvale, CA, or Seattle, WA.

Responsibilities

• 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, and drop-off images into structured quality signals that help verify orders, prevent delivery defects, and improve issue resolution.
• Design and run rigorous online experiments, production monitoring, and model iteration to continuously improve performance.
• Partner closely with software engineers, product managers, data scientists, and platform teams to bring new machine learning capabilities into production at scale.

Skills

Production ML

PyTorch

Spark

Airflow

Python

Deep Learning

Reinforcement Learning

Optimization

LLMs/VLMs

End-to-end ML

Ownership

Education

Bachelor's, Master's, or PhD

Tools

Airflow

Spark

Job description

DoorDash is seeking a Machine Learning Engineer to own end-to-end ML systems on the Drive team, from feature engineering to deployment and monitoring. You will work across ETA prediction, optimization, and AI-native experiences using LLMs and VLMs to improve logistics outcomes for merchants and dashers.

You will collaborate with software engineers, PMs, and data scientists to scale models, run experiments, and maintain production readiness in a fast-paced, data-driven environment.