AI Research Intern

oneapp; United States; Hybrid; USD 34,000 - 55,000; Full time

Benefits offered by this job

Downtown NYC office

Hybrid work 3-4 days/week

Professional development activities

Team social outings

Job summary

oneapp is seeking a final-year graduate student for an applied AI research internship in New York City. You will explore transformer and graph-based models on fintech data, train and evaluate prototypes, and deliver a working demonstration with real customer impact.

The role embeds with product, engineering, and analytics teams to improve customer experience and business outcomes. Hybrid onsite 3-4 days weekly from June 21 to August 13, 2027.

Qualifications

• Final-year master's or PhD student graduating Fall 2027 or Spring 2028.
• Strong grounding in deep learning and model training.
• Experience implementing or fine-tuning models in PyTorch.
• At least one prior internship or project in ML/DS.

Responsibilities

• Design, train, and evaluate foundation models on real customer, transaction, and merchant datasets using transformer or graph architectures.
• Set up rigorous experiments, document results, and iterate based on quantitative findings.
• Translate research directions into a functioning prototype for product and engineering teams to build on.
• Collaborate with product, engineering, and analytics teams to align modeling with customer-facing outcomes.
• Communicate findings through summaries, presentations, and code.

Skills

Deep learning fundamentals

Experiment design

Communication

Education

Master's or PhD in engineering/CS

Tools

PyTorch

Graph neural networks

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.