Stripe is hiring a Master's ML Engineer Intern in Toronto!
Stripe is hiring Master's-level machine learning engineering interns in Toronto for summer 2027. The internship sits across Stripe's Applied ML, Data Science, Risk, and Payments groups, where interns contribute to projects that support products such as fraud detection, customer support, user-behavior prediction, the Stripe Assistant, and the Stripe Foundation Model. This is a hands-on role for students who want to apply ML to large-scale financial infrastructure and see their work move from design through training, improvement, and launch. Interns collaborate with multiple teams, learn unfamiliar systems quickly, and communicate progress clearly. Candidates should be pursuing a Master's in computer science, machine learning, or a related field, expect to graduate in winter 2027 or spring/summer 2028, and intend to return to their degree program after the internship. Applications should include a resume or LinkedIn profile plus examples of relevant work, such as GitHub repositories, StackOverflow contributions, or project portfolios.
Key responsibilities
- Build and deploy large-scale machine learning systems that create measurable business value across Stripe domains.
- Design, train, iterate on, and launch ML models through an end-to-end development process.
- Write production-scale ML code that supports Stripe's economic infrastructure for businesses worldwide.
- Work with cross-functional teams, incorporate feedback, and proactively resolve technical challenges.
- Learn new tools and methods quickly, ask useful questions, and communicate work status effectively.
Key requirements
- Currently pursuing a Master's degree in Computer Science, Machine Learning, or a closely related field, with graduation expected in winter 2027 or spring/summer 2028.
- Hands-on programming and ML experience from projects, coursework, or research.
- Familiarity with Python, Scala, Spark, Pandas, NumPy, and Scikit-learn.
- Knowledge of supervised and unsupervised learning, ML operations, and potentially LLMs or reinforcement learning.
- Evidence of collaborative work and experience applying feedback from stakeholders.
- Ability to understand complex systems independently and learn unfamiliar systems proactively.
- Intent to return to the degree program after completing the internship.
- Preferred: published peer-reviewed research, strong code-review and testing habits, cross-language codebase experience, and excellent written communication.
- Seniority: Master's internship
- Work mode: Not specified
- Key skills: Python, Scala, Spark, Pandas, NumPy, Scikit-learn, supervised learning, unsupervised learning, ML operations, Large Language Models, Reinforcement Learning
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