PhD Machine Learning Engineer, Intern

Stripe · Toronto · 5112 General University · listed October 30, 2026

The shape of it

Seniority
Internship
Where
Not stated
Requirements listed
8
Length
567 words

In the posting’s own words

Join us for an unforgettable summer internship and help shape the future of global commerce. At Stripe, you won't just be working on theoretical projects; you'll make a tangible impact on the world's economic infrastructure.

What it asks for · 8

  • A deep understanding of computer science, obtained through the pursuit of a PhD 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.
  • Demonstrated ability to work on collaborative projects, with experience in receiving and applying feedback from various stakeholders.
  • A proactive approach to learning unfamiliar systems and a demonstrated ability to understand complex systems independently.
  • Intent to return to the degree-program after the completion of the internship/co-op.
  • A detailed resume or LinkedIn profile showcasing your work history.
  • Examples of relevant work and your approach to learning, such as GitHub repositories, StackOverflow contributions, or other project portfolios.

Also a plus

  • 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 to clearly articulate your work to both team members and wider Stripe audiences.

What the job covers

  • 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.

Degree language

  • A deep understanding of computer science, obtained through the pursuit of a PhD in Computer Science, Machine Learning, or a closely related field, with the expectation of graduating in winter 2027 or spring/summer 2028.
  • Intent to return to the degree-program after the completion of the internship/co-op.

Tools and skills named

Models & research
  • Machine learning8×
  • LLM
  • Reinforcement learning
Data
  • NumPy
  • Pandas
  • Spark
Languages
  • Python
  • Scala
Frameworks
  • Spring
Ways of working
  • Testing

Words the posting leans on

  • machine learning6×
  • experience5×
  • projects5×
  • languages3×
  • models3×
  • systems3×
  • approach learning2×
  • computer science2×
  • demonstrated2×
  • domains2×
  • economic infrastructure2×
  • familiarity2×
  • feedback2×
  • programming2×
  • requirements2×
  • various2×

Counted from the posting after the mission statement and the legal notices are set aside. The ones near the top are the ones a screener is looking for.

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How this page was made

An automated read of a public job posting, fetched November 1, 2026 and last changed by Stripe on October 30, 2026. Every list above is pulled from the posting’s own sentences — nothing rewritten, nothing added, no judgment about the role or the company. Counts and seniority are read off the text by rule, so they can be wrong where the posting is unusual. The original is the only thing that binds. Openings close without warning; check the source before spending an evening on it.