Machine Learning Engineer, Drive

About the Role

The Drive Machine Learning team builds the prediction and intelligence systems that power DoorDash Drive, which handles deliveries placed through merchants' own channels using DoorDash's logistics network. This role focuses on end-to-end ownership of machine learning systems—from feature engineering and model development to experimentation, deployment, monitoring, and continuous iteration.

Drive presents unique machine learning challenges due to diverse merchant workflows, preparation patterns, and customer expectations. You’ll work on models that generalize across millions of deliveries while adapting to highly varied merchant behaviors.

Responsibilities

• 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 enhance 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, receipts, and drop-off images into structured quality signals for order verification and defect prevention.
• 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 deploy machine learning capabilities at scale.

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 ML models using modern deep learning frameworks (e.g., PyTorch) and distributed data processing technologies (e.g., Spark, Airflow).
• End-to-end experience building, deploying, monitoring, and maintaining production ML systems.
• Strong software engineering skills in Python and familiarity 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).
• Located or planning to relocate to San Francisco, CA, Sunnyvale, CA, or Seattle, WA.

Skills

• Strong sense of ownership, with the ability to take models from research to production.
• Comfort working in ambiguous environments where experimentation and iteration drive product decisions.
• Focus on both model quality and production reliability.
• Excitement to work across diverse ML techniques, including neural networks, optimization, and multimodal AI.
• Collaborative mindset across engineering, product, and data science teams.

Pay

The national base pay ranges for this position within the United States are as follows (localized based on work location and market conditions):

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

In addition to base salary, compensation includes equity grants. Factors such as skills, experience, qualifications, work location, and market conditions determine the starting pay.

Benefits

• Comprehensive medical, dental, and vision benefits.
• 401(k) plan with employer matching.
• 16 weeks of paid parental leave.
• Wellness benefits and commuter benefits match.
• Paid time off (PTO) and paid sick leave (80 hours/year for salaried roles; accrual-based for hourly roles).
• 11 paid holidays.
• Disability and basic life insurance.
• Family-forming assistance and mental health program.

Schedule

• Salaried roles: Flexible paid time off/vacation, plus 80 hours of paid sick time per year.
• Hourly roles:
  • Vacation accrued at ~1 hour for every 25.97 hours worked (e.g., ~6.7 hours/month for 40-hour weeks; ~3.4 hours/month for 20-hour weeks).
  • Paid sick time accrued at 1 hour for every 30 hours worked (e.g., ~5.8 hours/month for 40-hour weeks; ~2.9 hours/month for 20-hour weeks).