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. 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. 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. 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. Must be authorized to work in the U.S. for the duration of the internship. Actual compensation is based on various factors including but not limited to work location, and relevant skills and experience. The total compensation for this position may include an annual performance bonus (or other incentive compensation, as applicable), equity, and medical, dental, vision, and other benefits.