AI Research Intern

oneapp; United States; Hybrid; USD 89,000 - 134,000; Full time

Job summary

oneapp is seeking a final-year graduate student for an applied AI internship in New York City. You will explore transformer and graph architectures, run controlled experiments, and deliver a working prototype that demonstrates a chosen approach.

The role embeds with product, engineering, and applied science teams to improve customer experience and business outcomes. Ideal candidates are final-year masters or PhD students graduating in 2027/2028, with hands-on PyTorch experience and prior ML

Qualifications

• Master’s or PhD student expected to graduate Fall 2027 or Spring 2028.
• Solid understanding of deep learning, including attention and embeddings.
• Hands-on experience training or fine-tuning models in PyTorch or similar.
• Previous internship or project work in ML or software engineering.
• Strong collaboration and communication skills.

Responsibilities

• Design, train, and evaluate foundation models on real datasets.
• Set up rigorous experiments and document results.
• Translate research directions into a usable prototype.
• Collaborate with product, engineering, and analytics teams.
• Communicate findings via summaries, presentations, and code.

Skills

Deep learning fundamentals

PyTorch

Research experience

Experiment design

Communication

Education

Master's or PhD in engineering or computer science

Tools

PyTorch or equivalent framework

Job description

Role overview

This internship places a final-year graduate student on an applied AI research team investigating foundation models for financial services data. The intern will explore transformer-based and graph-based architectures, run controlled experiments, and deliver a working prototype that demonstrates a chosen approach. The work is embedded alongside product, engineering, and applied science teams, with the goal of improving customer experience, operational efficiency, and business outcomes.

Responsibilities

• Design, train, and evaluate foundation models on real customer, transaction, and merchant datasets, working with transformer and/or graph neural network architectures.
• Set up rigorous experiments, document results, and iterate quickly based on quantitative findings.
• Translate promising research directions into a functioning prototype that product and engineering teams can build on.
• Partner closely with product, engineering, and analytics colleagues to align modeling work with customer-facing outcomes.
• Communicate findings clearly through written summaries, presentations, and code, contributing to shared team knowledge.

Requirements

• Final-year master's or PhD student in engineering, computer science, or a closely related field, graduating in Fall 2027 or Spring 2028.
• Solid grasp of deep learning fundamentals including backpropagation, attention mechanisms, embeddings, optimization, and training dynamics.
• Hands-on experience implementing, training, or fine-tuning models in PyTorch or an equivalent framework, through research, coursework, or substantial personal projects.
• At least one prior internship in research, machine learning, or software engineering, or equivalent project experience.
• Availability for a full-time, 8-week program running June 21 through August 13, 2027, working onsite in a New York City office 3-4 days per week.
• Ownership mentality, comfort with fast-paced environments, and strong organizational and communication skills.

Nice to have

• Familiarity with transformer-based foundation model lifecycles, including pre-training, fine-tuning, inference, and architectural tradeoffs.
• Exposure to graph-based learning approaches 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 the form of publications, workshop papers, or open-source contributions.

Benefits and work setup

• Hands-on experience shipping a research prototype with potential for production impact at a high-growth fintech.
• Access to a downtown New York City office and structured professional development activities with team social outings.
• Hybrid in-office requirement of 3-4 days per week for the duration of the 8-week program.
• Visa sponsorship is not available for this position; candidates must be authorized to work without future sponsorship.
• Standard interview process includes an initial screen, talent partner conversation, technical or hiring manager round, team interview, and executive conversation.