PayPal is seeking to streamline compliance processes, strengthen risk mitigation, and empower a secure, trusted, and seamless financial experience worldwide by leveraging advanced machine learning and data-driven insights.
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.
- 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).
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.
Other
- Currently pursuing a PhD in Computer Science, Machine Learning, Statistics, or a related field.
- 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.
- Must be authorized to work in the U.S. for the duration of the internship.
- Excellent communication and collaboration skills, with the ability to present research to both technical and non-technical audiences.
