Machine Learning Engineer PhD Intern

Job Summary:

MLE PhD Intern plays a critical role in ensuring PayPal’s adherence to a complex and evolving landscape of local and global regulations. By leveraging advanced machine learning, cutting-edge research, and data-driven insights, we design innovative solutions that streamline compliance processes, strengthen risk mitigation, and empower PayPal to deliver a secure, trusted, and seamless financial experience worldwide.

Key Responsibilities:

• Gain hands-on experience working on real-world large language model (LLM) and machine learning projects within the domains of commerce, personalization, recommendation, and user behavior understanding.
• Assist in the fine-tuning, evaluation, and deployment of LLMs for tasks such as personalized recommendations, semantic search, and behavioral modeling.
• Collaborate with experienced engineers, data scientists, and product experts to translate business requirements into actionable LLM and ML-driven solutions.
• Analyze data, build prototypes, and explore new methodologies to improve the effectiveness of personalization and recommendation systems.
• Contribute to the development and documentation of LLM training pipelines and model evaluation frameworks, ensuring reproducibility and maintainability.
• Present findings and recommendations to stakeholders across the organization, highlighting the business impact of personalization and LLM applications.
• Network with talented professionals and gain valuable insights into the world of financial technology, personalization, and applied machine learning.

Basic Requirements:

• Strong understanding of machine learning concepts, algorithms, and techniques (e.g., supervised learning, unsupervised learning, deep learning).
• Familiarity with large language models (e.g., GPT, LLaMA, Mistral) and techniques for fine-tuning, prompt engineering, or embeddings-based retrieval.
• Proven ability to work with Python, libraries like NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, and Hugging Face Transformers.
• Experience with data analysis, cleaning, and wrangling.
• Excellent communication, collaboration, and problem-solving skills.
• A passion for learning and exploring new technologies.
• Highly motivated and proactive with a strong work ethic.

Program Specific Requirements:

• Currently pursuing a PhD in Computer Science, Machine Learning, Statistics, or a related field.
• Strong theoretical foundation in ML algorithms, optimization, and statistical learning theory.
• Demonstrated ability to implement and evaluate ML models using Python and libraries such as NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch.
• Experience conducting independent research, with publications in relevant ML/AI conferences or journals (preferred).
• Excellent communication and collaboration skills, with the ability to present research to both technical and non-technical audiences.
• Highly motivated, curious, and proactive in exploring new research directions.

Program Information and Requirements:

• This is a Summer 2026 PhD Internship (Spring and Fall sessions are not available).
• Must be enrolled in a PhD program at an accredited university, returning to studies after the internship.
• Must reside in the U.S. during the program.