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 MedTech, 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. Learn more at jnj.com

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

Job Description:

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.

Learn more at https://www.jnj.com/innovative-medicine

About the role

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.

Key responsibilities

• 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.

What you’ll learn / deliver

• 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.

Required qualifications

• 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 computer vision fundamentals and of how to train and evaluate models.

• Experience running experiments, tracking results, and performing basic troubleshooting and analysis.

• Strong written and verbal communication skills.

Preferred qualifications

• Prior research or project experience related to modular model design, predictive/alignment methods for representation learning, or representation learning using unlabeled data.

• Experience working with medical imaging or multi-modal visual data (including video) and familiarity with common preprocessing challenges.

• Experience with training models at scale and with experiment management practices.

• Understanding of clinical evaluation metrics and concerns around generalization and robustness in medical imaging.

• Publications, open-source contributions, or a portfolio demonstrating relevant work.

Permanently authorized to work in the U.S., must not require sponsorship of an employment visa (e.g., H-1B or green card) at the time of application or in the future.Students currently on CPT, OPT, or STEM OPT usually requires future sponsorship for long term employment and do not meet the requirements for this program unless eligible for an alternative long-term status that does not require company sponsorship.

The expected pay range for this position is between $23.00 per hour and $51.50 per hour but will be based on candidate's program year, discipline, degree and/or experience. Co-Ops/Interns are eligible to participate in Company sponsored employee medical benefits in accordance with the terms of the plan. Co-Ops and Interns are eligible for the following sick time benefits: up to 40 hours per calendar year; for employees who reside in the State of Washington, up to 56 hours per calendar year. Co-Ops and Interns are eligible to participate in the Company’s consolidated retirement plan (pension). 

For additional general information on Company benefits, please go to:  https://www.careers.jnj.com/employee-benefits 

This job posting is anticipated to close on 03/16/2026.  The Company may however extend this time-period, in which case the posting will remain available on https://www.careers.jnj.com to accept additional applications.

Ineligibility for severance.

Johnson & Johnson is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, protected veteran status or other characteristics protected by federal, state or local law. We actively seek qualified candidates who are protected veterans and individuals with disabilities as defined under VEVRAA and Section 503 of the Rehabilitation Act. 

Johnson & Johnson is committed to providing an interview process that is inclusive of our applicants’ needs. If you are an individual with a disability and would like to request an accommodation,  external applicants please contact us via https://www.jnj.com/contact-us/careers . Internal employees contact AskGS to be directed to your accommodation resource.

Required Skills:

Preferred Skills:

The anticipated base pay range for this position is :

$31.00/hr to $59.00/hr

Additional Description for Pay Transparency:

The expected pay range for this position is between $31.00 per hour and $59.00 per hour but will be based on candidate's program year, discipline, degree and/or experience. Co-Ops/Interns are eligible to participate in Company sponsored employee medical benefits in accordance with the terms of the plan. Co-Ops and Interns are eligible for the following sick time benefits: up to 40 hours per calendar year; for employees who reside in the State of Washington, up to 56 hours per calendar year. Co-Ops and Interns are eligible to participate in the Company’s consolidated retirement plan (pension).