Machine Learning Engineer, Drive

About the role

The Machine Learning Engineer on the Drive team will 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 to improve reliability for merchants and consumers.
• Develop deep learning models leveraging 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), such as transforming pickup photos, item verification flows, receipts, and drop-off images into structured quality signals to 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.

You’ll work across traditional machine learning, deep learning, reinforcement learning, optimization, and multimodal AI while solving challenging logistics problems at DoorDash.

About the team

DoorDash Drive powers deliveries placed through merchants' own channels—including 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. The team has significant opportunities to improve prediction accuracy, optimize logistics decisions, and build AI-native experiences that directly enhance merchant, consumer, and dasher outcomes.

Requirements

• 5+ years of industry experience building and shipping production machine learning systems with measurable business impact (Bachelor's, Master's, or PhD).
• 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 / Operations Research
  • 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.
• Located or planning to relocate to San Francisco, CA, Sunnyvale, CA, or Seattle, WA.

Skills

• Enjoy solving large-scale machine learning problems that directly impact millions of deliveries.
• Strong sense of ownership and ability to take models from research through production.
• Comfortable working in ambiguous environments where experimentation and iteration drive product decisions.
• Care about both model quality and production reliability.
• Excited to work across a diverse set of ML techniques—from neural networks and optimization to multimodal AI.
• Collaborate well across engineering, product, and data science teams.

Pay

The national base pay ranges for this position within the United States, including Illinois and Colorado, are as follows:

• I4: $137,100—$201,600 USD
• I5: $167,800—$246,800 USD
• I6: $203,500—$299,300 USD

The successful candidate's starting pay will fall within the pay range listed and is determined based on job-related factors including skills, experience, qualifications, work location, and market conditions. In addition to base salary, compensation includes opportunities for equity grants.

Benefits

• Comprehensive benefits package including:
  • 401(k) plan with employer matching
  • 16 weeks of paid parental leave
  • Wellness benefits and commuter benefits match
  • Paid time off and paid sick leave (compliant with applicable laws, e.g., Colorado Healthy Families and Workplaces Act)
  • Medical, dental, and vision benefits
  • 11 paid holidays
  • Disability and basic life insurance
  • Family-forming assistance and a mental health program

Schedule

For salaried roles:

• Flexible paid time off/vacation, plus 80 hours of paid sick time per year.

For hourly roles:

• Vacation accrued at about 1 hour for every 25.97 hours worked (e.g., about 6.7 hours/month if working 40 hours/week; about 3.4 hours/month if working 20 hours/week).
• Paid sick time accrued at 1 hour for every 30 hours worked (e.g., about 5.8 hours/month if working 40 hours/week; about 2.9 hours/month if working 20 hours/week).