2025 Computer Vision and Machine Learning Research Summer Associate

JPMorgan Chase - New York, NY

Full-time - Intern

New York, NY

Credit Intermediation and Related Activities

About the position

The 2025 Computer Vision and Machine Learning Research Summer Associate position at JPMorgan Chase & Co. offers a unique opportunity to engage in cutting-edge research within the Global Technology Applied Research (GTAR) division. The role focuses on advancing trustworthy computer vision and machine learning technologies, providing innovative research solutions, and contributing to the firm's intellectual property. Interns will receive mentorship and support for professional growth, with the potential for full-time employment upon successful completion of the program.

Responsibilities

• Advance the field of trustworthy computer vision and machine learning
• Provide novel research solutions to problems faced by internal project teams
• Work with other researchers to document findings in scientific papers
• Contribute to JPMC's IP by pursuing necessary protections of generated IP

Requirements

• 1+ years of experience with computer vision and machine learning algorithms and applications
• Experience in one or more following domains: 2D/3D computer vision, neural radiance fields, efficient video analytics, edge computing, computer vision for AR/VR
• Trustworthy/Responsible machine learning, e.g., fairness, privacy, prediction uncertainty, predictive multiplicity, interpretability, and machine unlearning, LLM alignment
• Excellent knowledge of Python programming, and familiarity with related deep learning frameworks
• Experience in scientific technical writing, as well as grant applications or research proposals
• Working knowledge of common research evaluation frameworks and techniques
• Excellent analytical, quantitative and problem solving skills and demonstrated research ability
• Strong communication skills and the ability to present findings to a non-technical audience
• Enrolled in a master's or Ph.D. degree program in math, science, engineering, computer science or related fields

Nice-to-haves

• Preference is given to candidates with a strong publication record.