PhD Machine Learning Engineer, Intern

at Stripe

📍 Toronto, Canada

CAD 133,100-162,300 per year

INTERN

✅ On-site

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

Communication @ 3 Data Science GitHub @ 3 LLM Machine Learning @ 3 Pandas @ 2 Payments Python @ 2 Reinforcement Learning @ 3 Scala @ 2 Spark @ 2 scikit-learn @ 2

Details

Stripe's Applied ML, Data Science, Risk, and Payments organizations are offering PhD machine learning engineering internships for summer 2027. Interns will contribute to critical projects involving foundation models for fraud detection, enhanced support, predicting user behavior, the Stripe Assistant, and the Stripe Foundation Model.

Responsibilities

Develop and deploy large-scale machine learning systems that drive business value across various domains.
Design, train, improve, and launch machine learning models.
Write production-scale machine learning models for Stripe's economic infrastructure products.
Collaborate across teams, incorporate feedback, and proactively solve challenges.
Learn new technologies and approaches quickly, ask insightful questions, and communicate work status effectively.

Requirements

Pursuing a PhD in Computer Science, Machine Learning, or a closely related field, with an expected graduation date in winter 2027 or spring/summer 2028.
Practical programming and machine learning experience through projects, classwork, or research.
Familiarity with Python, Scala, Spark, Pandas, NumPy, and Scikit-learn.
Expertise in machine learning areas including supervised and unsupervised learning techniques and machine learning operations; experience with large language models or reinforcement learning may be relevant.
Ability to work collaboratively, receive feedback, and apply it effectively.
Proactive approach to learning unfamiliar systems and independently understanding complex systems.
Intent to return to the degree program after completing the internship or co-op.

Preferred Qualifications

Two years of university education or equivalent experience, with in-depth knowledge in specific machine learning 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.

Application Requirements

A detailed resume or LinkedIn profile showcasing work history.
Examples of relevant work and approaches to learning, such as GitHub repositories, Stack Overflow contributions, or other project portfolios.