Master's 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

Who We Are

Stripe is a financial infrastructure platform for businesses. Millions of companies use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Its mission is to increase the GDP of the internet.

Internship Overview

Stripe's Applied ML, Data Science, Risk, and Payments organizations offer Master's machine learning engineering internships for the summer of 2027. Interns will contribute to critical projects that enhance Stripe's products, including foundation models for fraud detection, enhanced support, and user behavior prediction.

The role involves challenging problems at the intersection of finance, technology, and data. Projects may include the Stripe Assistant and the Stripe Foundation Model, which use machine learning to improve how businesses interact with financial services and data.

Responsibilities

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

Requirements

Pursuing a Master's degree in Computer Science, Machine Learning, or a closely related field, with expected graduation in winter 2027 or spring/summer 2028.
Practical programming and machine learning experience through projects, coursework, or research.
Familiarity with Python, Scala, Spark, Pandas, NumPy, and Scikit-learn.
Expertise in supervised and unsupervised learning, ML operations, and potentially Large Language Models or Reinforcement Learning.
Ability to work collaboratively and receive and apply feedback from stakeholders.
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 working 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.