PhD ML Engineer Intern — Foundation Models

Sanics Jobs; New York, Northern (NY, KY); Hybrid; USD 70,000 - 100,000; Full time

Job summary

Stripe is offering PhD machine learning engineering internships for Summer 2026. You will tackle projects at the intersection of finance, technology, and data, focusing on foundation models for tasks like fraud detection and predicting user behavior.

Responsibilities include building and deploying large-scale ML systems, training and launching models, and collaborating across teams while learning rapidly and communicating progress clearly.

Qualifications

• Pursuing PhD with expected graduation in winter 2026 or spring/summer 2027.
• Practical programming experience in ML projects or research.
• Familiar with Python, Scala, Spark, Pandas, NumPy and Scikit-learn.
• Knowledge of supervised/unsupervised learning, ML ops, LLMs or RL beneficial.
• Ability to work on collaborative projects and absorb feedback.
• Self-driven learner capable of understanding complex systems.

Responsibilities

• Develop and deploy large-scale ML systems delivering business value.
• End-to-end design, training, improvement, and launch of models.
• Write production-scale ML models deployed to enable access for diverse businesses.
• Collaborate across teams and incorporate feedback.
• Rapidly learn new technologies and communicate status effectively.

Skills

Python

Scala

Spark

Pandas

NumPy

Scikit-learn

ML concepts

Supervised learning

Unsupervised learning

Education

PhD in Computer Science, ML, or related

Two years of university education or equivalent

Tools

GitHub

Job description

Stripe is offering PhD machine learning engineering internships for Summer 2026. You will tackle projects at the intersection of finance, technology, and data, focusing on foundation models for tasks like fraud detection and predicting user behavior.

Responsibilities include building and deploying large-scale ML systems, training and launching models, and collaborating across teams while learning rapidly and communicating progress clearly.