JPMorgan Computer Vision Machine Learning Research Summer Associate 

United States, New York, New York 

884686696

As a 2025 Computer Vision and Machine Learning Research Summer Associate within the Global Technology Applied Research (GTAR) division of JPMorgan Chase & Co., your professional growth and development will be supported throughout the internship program via project work related to your academic and professional interests, mentorship, interaction with senior stakeholders and more. Our areas of focus include computer vision and responsible machine learning. Full-time employment offers may be extended upon successful completion of the program.

Job 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 your findings in scientific papers
• Contribute to JPMC’s IP by pursuing necessary protections of generated IP

Required qualifications, capabilities, and skills

• 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 familiar to related deep learning frameworks
• Excellent knowledge of Python programming, and familiar to 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

Preferred qualifications, capabilities, and skills

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