PhD ML Research Engineer Intern – Compliance & Risk

PayPal, Inc.; San Jose (CA); Híbrido; USD 81.000 - 91.000; A tiempo parcial

Ventajas ofrecidas por este puesto de trabajo

Healthcare coverage

Balanced hybrid work model

Generous paid time off

Descripción de la vacante

PayPal, Inc. is seeking a Machine Learning Engineer PhD Intern in San Jose, CA, to join a team researching and deploying ML solutions that support compliance, risk mitigation, and secure financial experiences globally.

You will work with engineers, data scientists, and product teams to develop, evaluate, and deploy ML models, tackling complex problems with large datasets while communicating findings effectively.

Formación

• Pursuing a PhD in computer science, machine learning, statistics, or related field.
• Strong foundation in ML and statistics with research experience.
• Proficiency in Python and ML frameworks (PyTorch, TensorFlow, scikit-learn).
• Strong analytical, communication, and collaboration skills; publications preferred.

Responsabilidades

• Research, develop, and evaluate ML solutions for complex challenges.
• Design, build, and assess ML models for production use.
• Analyze data and run experiments to guide model development.
• Collaborate with engineers and data scientists to translate needs into scalable ML solutions.
• Contribute to deployment of ML pipelines and monitor model performance.
• Document experiments and communicate findings to technical and non-technical audiences.
• Stay informed on ML developments and assess relevance to PayPal products.

Conocimientos

Analytical thinking

Communication

Collaboration

Educación

PhD in CS/ML or related

Herramientas

Python

PyTorch

TensorFlow

scikit-learn

Descripción del empleo

PayPal, Inc. is seeking a Machine Learning Engineer PhD Intern in San Jose, CA, to join a team researching and deploying ML solutions that support compliance, risk mitigation, and secure financial experiences globally.

You will work with engineers, data scientists, and product teams to develop, evaluate, and deploy ML models, tackling complex problems with large datasets while communicating findings effectively.