AI​/ML Intern, Computer Vision

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 uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of 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. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit.

Job Function: Career Programs

Job Sub Function: Non-LDP Intern/Co-Op

Job Category: Career Program

All Job Posting Locations: New Brunswick, New Jersey, 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;
Washington, District of Columbia, United States of America

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.

Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way.

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We are seeking a motivated research intern to contribute to foundational R&D for large-scale, multi-modal visual models applied to medical and clinical imaging. The role focuses on three broad, generic research areas: (1) modular model architectures that enable specialization and efficiency through multiple subcomponents, (2) predictive and alignment-based approaches that improve contextual and temporal understanding across images and videos, and (3) improvements to representation-learning pipelines that make better use of unlabeled data and modality-specific preprocessing.

Projects will involve medical imaging modalities such as histopathology, X-rays, endoscopy video, and dermatology imaging.

• Design, implement, and evaluate scalable modular model architectures that allow specialization and efficient use of computation.
• Develop and test methods that learn richer contextual and temporal representations by predicting or aligning different views, frames, or modalities.
• Improve representation-learning pipelines by experimenting with data preparation strategies, augmentation approaches, training schedules, and hyperparameter settings to increase robustness across modalities.
• Build reproducible training and evaluation workflows and run experiments at scale; maintain clear experiment logs and analyses.
• Measure model effectiveness on clinically relevant downstream tasks (e.g., classification, detection, segmentation, retrieval, temporal reasoning) and produce comparison reports and ablation studies.
• Collaborate with data engineers, clinicians, and researchers to curate and prepare datasets while following privacy and governance requirements.
• Produce well-documented code, experiment artifacts, internal reports, and, where appropriate, contribute to technical write-ups or presentations.

• Hands-on experience designing and scaling foundation models for medical imaging.
• Practical skills in large-scale experimentation, reproducibility, and domain-specific evaluation.
• Deliverables may include reproducible code repositories, experiment notebooks, benchmark results, ablation studies, and a final research report or presentation. Strong contributions could lead to co-authorship on technical reports.

• Currently pursuing or recently completed a Bachelor’s, Master’s, or PhD in Computer Science, Engineering, Applied Mathematics, or a related field.
• Strong programming ability (Python) and experience with common machine learning libraries.
• Solid understanding of machine learning and…