Machine Learning Engineer Intern: Build Scalable AI

PayPal, Inc.; San Jose (CA); Híbrido; USD 69.000 - 81.000; Jornada completa

Descripción de la vacante

PayPal is seeking a Machine Learning Engineer Intern to join our AI/ML teams across PayPal, Braintree, Venmo, and Paydiant. You will contribute to data preparation, model development, experimentation, and deployment under guidance from experienced engineers and data scientists.

The role emphasizes learning, collaboration, and building scalable ML pipelines, with exposure to production-grade systems and cross-product projects.

Formación

• Experience with at least one programming language (Python, Java, C++ or similar).
• Familiarity with machine learning concepts through coursework, research, internships, or personal projects.
• Exposure to machine learning frameworks such as scikit-learn or TensorFlow.

Responsabilidades

• Assist with developing, testing, and optimizing machine learning models and algorithms for business and customer needs.
• Prepare, clean, and analyze datasets, helping assess data quality and identify patterns that support model development.
• Run experiments and evaluate model performance using appropriate methods and metrics.
• Collaborate with engineers and data scientists to support scalable ML pipelines and the deployment and monitoring of models in production.
• Write clean, maintainable code and document experiments, results, and technical decisions.
• Participate in code and design reviews, follow team standards and development practices, and incorporate feedback.

Conocimientos

Python

Java

C++

ML concepts

Problem solving

Educación

Bachelor's or Master's in CS/ML/Data Science

Herramientas

scikit-learn

TensorFlow

Descripción del empleo

PayPal is seeking a Machine Learning Engineer Intern to join our AI/ML teams across PayPal, Braintree, Venmo, and Paydiant. You will contribute to data preparation, model development, experimentation, and deployment under guidance from experienced engineers and data scientists.

The role emphasizes learning, collaboration, and building scalable ML pipelines, with exposure to production-grade systems and cross-product projects.