Master's ML Intern: Build & Deploy Foundation Models

Fdetimes; San Francisco (CA); On-site; USD 34,000 - 48,000; Full time

Job summary

Stripe is offering a Master’s machine learning engineering internship in San Francisco for Summer 2027. You’ll work on foundational models used for tasks like fraud detection and predicting user behavior, contributing to Stripe’s financial infrastructure and data-driven products.

You’ll tackle end-to-end ML challenges, develop production-scale models, and collaborate across orgs while learning new technologies and communicating progress effectively.

Qualifications

• Pursuing a Master's degree in Computer Science, ML, or a closely related field with expected graduation winter 2027 or spring/summer 2028.
• Experience with programming and ML via projects, classes, or research; familiarity with Python, Scala, Spark and libraries Pandas, NumPy, Scikit-learn.
• Expertise in ML areas such as supervised/unsupervised learning, ML ops; experience with Large Language Models or Reinforcement Learning is a plus.
• Ability to work on collaborative projects, and effectively incorporate feedback from stakeholders.
• Proactive in learning unfamiliar systems and able to understand complex systems independently.

Responsibilities

• Develop and deploy large-scale ML systems across domains.
• End-to-end design, training, improvement, and deployment of ML models.
• Write production-scale ML models deployed to enable global access to financial infrastructure.
• Collaborate across teams to incorporate feedback and solve challenges.
• Rapidly learn new technologies and communicate status clearly.

Skills

Python

Scala

Spark

Pandas

NumPy

Scikit-learn

ML concepts

Collaborative work

Independent learning

Education

Master's degree in Computer Science

Job description

Stripe is offering a Master’s machine learning engineering internship in San Francisco for Summer 2027. You’ll work on foundational models used for tasks like fraud detection and predicting user behavior, contributing to Stripe’s financial infrastructure and data-driven products.

You’ll tackle end-to-end ML challenges, develop production-scale models, and collaborate across orgs while learning new technologies and communicating progress effectively.