Machine Learning Intern (Masters)
DoorDash
This job at a glance
- Who can apply
- New grads and up to 1 year · Master's degree
- Must know
- Machine learningComputer visionData analysisNLPBudgetingPython
- Work
- On-site in San Francisco · internship
- Pay
- Not stated by the employer.
- Skill demand
- Machine learning85%Data analysis16%Computer vision15%NLP15%of 99 similar entry-level jobs posted in the US this week
- Listed
- Nov 8, 2026 · still open
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About the Role
As a Machine Learning Engineer intern at DoorDash, you'll tackle new challenges in machine learning and artificial intelligence. You will conduct research that can be applied across DoorDash engineering teams, engage in external collaborations and mentoring, and perform research in the following areas:
- Auction
- Game theory
- Recommender systems
- Ranking
- AdTech
- Computer Vision
- Causal Inference
- Big data analytics
This is a 12-week summer internship program.
Key Responsibilities
- Use cutting-edge research in ML/AI, NLP, RecSys, Ranking, Computer Vision, Causal Inference, Ad Tech, Graph analysis to solve real-world problems across discovery, ads, forecasting, fulfillment, and search experiences at DoorDash.
- Contribute and execute on research ideas that can be applied to improve product experience at DoorDash.
- Collect, analyze, and synthesize findings from data and use these insights to build relevant ML models.
- Write clean, efficient, and sustainable code.
Requirements & Tech Stack
- Mastery of at least one systems language: Java, C++, Python, Kotlin, GoLang.
- Mastery of at least one ML framework: Tensorflow, Pytorch, MLFlow.
Qualifications
- Working towards a Masters degree in Computer Science, ML, NLP, Statistics, Information Sciences, or a related field.
- Graduating between Fall 2027 & Summer 2028.
- Experience in research and in solving analytical problems.
- Strong communicator and team player.
- Passion for applied ML and the DoorDash product.
- Ideally, experience with publications in machine learning, AI, data science, data analytics, statistics, or related technical fields.