Intern Data Science , Computer Vision

Overview

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and Med Tech, we are positioned to innovate across the full spectrum of healthcare solutions today to deliver breakthroughs tomorrow and profoundly impact health for humanity.

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As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual and we respect the diversity and dignity of our employees and recognize their merit.

Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow. Fueled by innovation at the intersection of biology and technology, we’re developing the next generation of smarter, less invasive, more personalized treatments.

We are searching for the best talent for Data Science Intern, Computer Vision to be in PA Spring House.

• The Intern term is from June to August, 2026.
• Full time requirement (40 hours per week).

Job Function:
Non-LDP Intern/Co-Op

Job Category:
All Job Posting Locations:

Locations: Brisbane, California, United States of America;
Cambridge, Massachusetts, United States of America;
Raritan, New Jersey, United States of America;
San Diego, California, United States of America;
Spring House, Pennsylvania, United States of America;
Titusville, New Jersey, United States of America

The Computer Vision team at Johnson & Johnson Innovative Medicine is seeking a PhD student for a 10-12 week summer research internship. We are looking for a knowledgeable, self-motivated, and creatively inclined graduate student with a strong interest in computer vision, deep learning, and medical image analysis. The internship will focus on leveraging and potentially extending histopathology visual features describing the disease microenvironment of inflammatory conditions to develop machine learning models for predicting clinical trial endpoints and/or metrics of disease state.

This internship offers the opportunity to work within a cross-functional team in a pharmaceutical environment. In addition to gaining technical exposure through the various projects the team is undertaking, you will also gain valuable insights into how algorithms are developed to support clinical trials under the mentorship of experienced scientists, while contributing to initiatives aimed at improving global health.

• Design, develop, implement and validate innovative deep learning solutions addressing high-priority computer vision challenges in medical imaging.
• Collaborate closely with cross-functional teams of scientists, engineers, and clinicians to advance algorithm and product development to improve project outcomes.
• Clearly communicate complex technical methodologies and present findings to diverse audiences and stakeholders to support informed decision-making.
• Prepare and draft manuscripts outlining development methodologies to contribute to the scientific community.

• Completion of Undergraduate Freshman year at an accredited University is required.
• Actively pursuing a PhD in a field related to deep learning and computer vision, such as computer science, medical informatics, biomedical engineering, electrical engineering, or a related discipline.
• Have a cumulative GPA of 2.8 or higher, which is reflective of all college coursework.
• Strong publication record with the ability to effectively communicate technical work to a broad audience. Publications in conferences such as CVPR, ICLR, ICCV, ICML, MICCAI, ACL, and NEURIPS are preferred but not mandatory.

• Strong communication & interpersonal skills.
• Ability to work in a matrix organization and participate effectively both independently and as part of multiple teams.
• Capaci…