Stripe is hiring a PhD ML Engineer Intern in Toronto!
Stripe is hiring PhD machine learning engineering interns for summer 2027 in its Toronto office, placing them within the Applied ML, Data Science, Risk, and Payments organizations. Interns work on problems at the intersection of finance and technology, contributing to foundation models that power many downstream tasks — fraud detection, customer support, and predicting user behaviour — as well as projects such as the Stripe Assistant and the Stripe Foundation Model. The emphasis is on shipping real production systems rather than theoretical exercises, with interns expected to own models end to end and see them launched to support businesses worldwide.
Key responsibilities
- Build and deploy large-scale machine learning systems that deliver measurable business value across several domains
- Own the full lifecycle of model work: design, training, iteration, and launch
- Write production-grade ML code that ships to Stripe's global economic infrastructure
- Partner with other teams, incorporate feedback, and proactively resolve blockers
- Pick up unfamiliar tools and systems quickly, ask sharp questions, and keep stakeholders informed on progress
Key requirements
- Pursuing a PhD in computer science, machine learning, or a closely related discipline, with graduation expected in winter 2027 or spring/summer 2028
- Hands-on programming and ML experience from projects, coursework, or research, using Python, Scala, or Spark alongside Pandas, NumPy, and Scikit-learn
- Grounding in supervised and unsupervised learning and ML operations; exposure to large language models or reinforcement learning is a plus
- Track record of collaborative work and of applying feedback from multiple stakeholders
- Self-directed learner who can make sense of complex systems independently
- Planning to return to the degree program after the internship
- Preferred: published peer-reviewed work at top-tier venues, clean pull requests with strong test coverage, comfort moving across codebases and languages, and strong written communication
- Application materials: a detailed resume or LinkedIn profile plus examples of relevant work such as GitHub repos or project portfolios
- Seniority: Internship
- Work mode: Not specified
- Key skills: Python, Scala, Spark, Pandas, NumPy, Scikit-learn, Supervised learning, Unsupervised learning, MLOps, Large Language Models, Reinforcement Learning, Production ML systems
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