DoorDash USA
Machine Learning Intern (PhD) - Summer 2027
About this role
DoorDash is seeking PhD-level machine learning interns for a 12-week summer program across multiple US offices to develop ML/AI solutions for their logistics and marketplace platform. You'll work on research in areas like recommender systems, ranking, computer vision, and causal inference while contributing production-quality code and building models that improve real user experiences.
What you'll do
- Conduct applied machine learning research across discovery, ads, forecasting, fulfillment, and search
- Develop and execute on research ideas with real product impact at DoorDash
- Collect, analyze, and synthesize data to build relevant ML models
- Write clean, efficient, and sustainable production-quality code
- Collaborate with cross-functional engineering teams on applied research
- Engage in mentoring and external research collaborations
What they're looking for
- Machine learning and AI fundamentals
- Python, Java, C++, Kotlin, or GoLang
- TensorFlow, PyTorch, or MLFlow
- Recommender systems and ranking algorithms
- Computer vision or NLP
- Causal inference and statistical analysis
- Big data analytics
- Research and problem-solving methodology
Benefits
- 401(k) plan with employer matching
- 16 weeks of paid parental leave
- Comprehensive medical, dental, and vision coverage
- Wellness benefits and mental health program
- Commuter benefits match
- Flexible paid time off and paid sick leave
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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
- Tell us about a machine learning research project you've published or contributed to—what was your specific role and impact?
- How have you applied cutting-edge ML techniques like causal inference or graph analysis to solve real-world problems?