ONE Finance
AI Research Intern
United States (Remote) · Remote · Internship · Posted Sep 21, 2026
- Experience
- At least one previous internship in research, machine learning, or software engineering, or substantial personal or academic projects.
- Education
- Final year of a master’s or PhD program in Engineering or a related field; expected graduation in Fall 2027 or Spring 2028.
- Visa sponsorship
- OnePay will not sponsor applicants who need employment sponsored visas for this position. Future sponsorship will not be considered.
About the job
Researches and prototypes transformer- and graph-based foundation models using customer, transaction, and merchant data, evaluating experiments and developing a working prototype.
What you'll do
- Research, design, and train foundation models on OnePay data.
- Run and rigorously evaluate experiments, turning promising results into a working prototype.
- Collaborate with Product, Engineering, and Analytics teams on customer experience and business outcomes.
What they ask for
- Final-year master’s or PhD student in Engineering or a related field, expected to graduate in Fall 2027 or Spring 2028.
- Strong understanding of deep learning fundamentals and experience implementing, training, or fine-tuning models; comfortable with PyTorch or an equivalent framework.
- At least one prior internship in research, machine learning, or software engineering, or substantial personal or academic projects.
- Available for the full-time, 8-week program June 21–August 13, 2027, and to work in the New York City office at least 3–4 days per week.
Nice to have
- Familiarity with transformer foundation models or graph-based learning and graph foundation models.
- Coursework or projects involving Large Language Models or Generative AI; research publications, workshop papers, or open-source contributions.
Pay
$32 per hour
Tools
PyTorch, transformer-based architectures, graph-based architectures, GNNs, Large Language Models, Generative AI
AI focus
Research and prototype foundation models, including transformer- and graph-based models, applied to customer, transaction, and merchant data.
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