Master's Machine Learning Engineer, Intern

About the Internship

Stripe is offering Master's machine learning engineering internships for the summer of 2027. This is an opportunity to contribute to critical projects that enhance Stripe's products, focusing on areas such as foundation models for tasks like fraud detection, enhanced support, and predicting user behavior. Interns will tackle challenging problems at the intersection of finance, technology, and data, working on creative projects like the Stripe Assistant and the Stripe Foundation Model.

Key Responsibilities

• Develop and deploy large-scale machine learning systems that drive significant business value.
• Engage in the end-to-end process of designing, training, improving, and launching machine learning models.
• Write production-scale ML models for deployment to enable economic infrastructure access for businesses globally.
• Collaborate across teams to incorporate feedback and proactively seek solutions.
• Rapidly learn new technologies and approaches, asking insightful questions and communicating work status effectively.

Minimum Requirements

• Pursuing a Master's degree in Computer Science, Machine Learning, or a closely related field, expecting to graduate 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 machine learning areas such as supervised and unsupervised learning techniques, ML operations, and possibly experience in Large Language Models or Reinforcement Learning.
• Demonstrated ability to work on collaborative projects and receive/apply feedback.
• Proactive approach to learning unfamiliar systems and understanding complex systems independently.
• Intent to return to the degree program after internship completion.

Preferred Qualifications

• Two years of university education or equivalent experience, with in-depth knowledge in specific domains of machine learning.
• Published and presented peer-reviewed articles in top-tier venues.
• Experience in 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.

Application Requirements

• A detailed resume or LinkedIn profile showcasing work history.
• Examples of relevant work and approach to learning (e.g., GitHub repositories, StackOverflow contributions, project portfolios).