Master's Machine Learning Engineer, Intern
A summer 2027 Master's-level machine learning internship at Stripe (SF, NYC, or Seattle), where interns design, train, and deploy production-scale ML systems across Applied ML, Risk, and Payments — including fraud detection, the Stripe Assistant, and the Stripe Foundation Model. Core technologies include Python, Scala, Spark, Pandas, NumPy, and Scikit-learn.
- Compensation
- Not disclosed
- Location
- San Francisco, New York City, Seattle
- Work setup
- Not specified
- Seniority
- Intern
- Employment
- Internship
- Experience
- Not specified
- Category
- Ml / AI
- Posted
Skills
Must have
- Master's degree in Computer Science, Machine Learning, or closely related field, graduating winter 2027 or spring/summer 2028
- Practical programming and ML experience (projects, classwork, research); familiarity with Python, Scala, Spark, Pandas, NumPy, Scikit-learn
- Expertise in supervised/unsupervised learning and ML operations; possibly experience with Large Language Models or Reinforcement Learning
- Demonstrated ability to work on collaborative projects and apply feedback from stakeholders
- Proactive approach to learning unfamiliar systems and understanding complex systems independently
- Intent to return to degree program after completing the internship/co-op
- Submit a detailed resume/LinkedIn profile plus examples of relevant work (GitHub repos, StackOverflow, project portfolios)
Nice to have
- Two years of university education or equivalent experience with in-depth knowledge in specific ML domains
- Published and presented peer-reviewed articles in top-tier venues
- Experience writing high-quality pull requests, maintaining good test coverage, and completing projects with minimal defects
- Familiarity with navigating new codebases and managing work across different programming languages
- Excellent written communication skills for articulating work to team members and wider audiences
Full description
Who we are
About Stripe
Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.
What you’ll do
About the internship
Stripe's Applied ML, Data Science, Risk, and Payments organizations are excited to offer Master's machine learning engineering internships for the summer of 2027. This is an exceptional opportunity to contribute to critical projects that directly enhance Stripe's suite of products, focusing on areas such as foundation models used for dozens of tasks e.g. fraud detection, enhanced support, and predicting user behavior.
As an intern, you'll tackle challenging problems at the intersection of finance, technology, and data. You'll have the chance to work on creative projects like the Stripe Assistant and the Stripe Foundation Model, which leverage machine learning to revolutionize how businesses interact with financial services and data.
Responsibilities
- Develop and deploy large-scale machine learning systems that drive significant business value across various domains.
- Engage in the end-to-end process of designing, training, improving, and launching machine learning models.
- Write production-scale ML models that will be deployed to help Stripe enable economic infrastructure access for a diverse range of businesses globally.
- Collaborate across teams to incorporate feedback and proactively seek solutions to challenges.
- Rapidly learn new technologies and approaches, demonstrating a strong ability to ask insightful questions and communicate the status of your work effectively.
Who you are
Minimum requirements
- A deep understanding of computer science, obtained through the pursuit of a Master's degree in Computer Science, Machine Learning, or a closely related field, with the expectation of graduating in winter 2027 or spring/summer 2028.
- Practical experience with programming and machine learning, evidenced by projects, classwork, or research. Familiarity with languages such as Python, Scala, Spark and libraries such as Pandas, NumPy, and Scikit-learn.
- Expertise in areas of machine learning such as supervised and unsupervised learning techniques, ML operations, and possibly experience in Large Language Models or Reinforcement Learning.