AI Research Intern (8 Week Internship)

Join OnePay as an AI Research Intern for an 8-week program from June 21 to August 13, 2027. You will research and prototype foundation models applied to customer, transaction, and merchant data. This role will have a significant impact on OnePay's vision by enhancing customer experience, driving business outcomes, and improving operational efficiency. You will work closely with the product, engineering, and applied science teams and report to the Head of AI/ML.

• Research, design, and train foundation models on OnePay's data, working with transformer and/or graph-based architectures.
• Run experiments, evaluate results rigorously, and turn promising findings into a working prototype.
• Collaborate with Product, Engineering, and Analytics teams to ensure the implementation of models provides a remarkable customer experience.

• Deep Learning Fundamentals: You have a strong grasp of how neural networks work under the hood, including backpropagation, attention, embeddings, optimization, and training dynamics
• Final-Year Graduate Student: You’re in the final year of a master’s or PhD program, expected to graduate in Fall 2027 or Spring 2028, majoring in Engineering or a related field
• Critical Thinking & Problem-Solving Skills: You enjoy tackling complex challenges, analyzing problems, and developing solutions. Clear communication of ideas is one of your strengths
• Availability: This is a full-time, 8-week program running June 21 - August 13, 2027. You are available to work in our New York City office at least 3-4 days a week throughout
• Proactive & Collaborative Attitude: You have an act-like-an-owner mentality, thrive in a fast-moving environment, and enjoy working with others to achieve collective goals
• Excited to Learn & Grow: You are passionate about being part of a rapidly growing start-up and look forward to expanding your skills in a real-world setting
• Prior Experience: You've completed at least one previous internship in research, machine learning, or software engineering, or have built substantial personal or academic projects you're excited to talk through
• You've implemented, trained, or fine-tuned models yourself through research, coursework, or personal projects, and you're comfortable working in PyTorch or an equivalent framework
• Organized & Detail-Oriented: You’re a keen listener with strong organizational skills, capable of managing multiple tasks efficiently
• Familiarity with transformer-based foundation model architectures, including pre-training, fine-tuning, inference, and the tradeoffs between approaches
• Exposure to graph-based learning or graph foundation models, such as GNNs, message passing, or representation learning on graph-structured data
• Coursework or projects involving Large Language Models or Generative AI
• Research output in any of the above: publications, workshop papers, or open-source contributions