Research Intern AI/ML & DH at Johnson & Johnson

Wayne State University; Cambridge (MA); On-site; USD 31,684 - 70,946; Full time

Benefits offered by this job

Company-sponsored employee medical benefits

Participation in Company’s consolidated retirement plan

Sick time benefits up to 40 hours

Job summary

A leading healthcare company is seeking a Research Intern in Cambridge, Massachusetts, from May to August 2026. This full-time role focuses on developing models for multimodal wearable sensor data, and requires a PhD candidate in Machine Learning or related fields with strong programming skills in Python. The intern will work on advanced machine learning techniques to impact digital health applications. Competitive hourly pay ranges from $23.00 to $51.50.

Qualifications

• Strong programming experience with Python and deep learning frameworks.
• Currently pursing a PhD with a cumulative GPA of 2.8 or higher.
• Experience working with large-scale datasets and research pipelines.

Responsibilities

• Train foundation models on large-scale unlabelled multimodal sensor datasets.
• Develop architectures using transformers and contrastive learning.
• Evaluate learned representations on downstream health tasks.

Skills

Programming with Python

Deep learning frameworks (PyTorch, TensorFlow)

Experience with large-scale datasets

Technical background in deep learning for time-series data

Education

Currently pursuing a PhD in Machine Learning, AI, Computer Science, Biomedical Engineering, or related fields

Completion of Undergraduate Freshman year at an accredited University

Job description

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.

About Innovative Medicine

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.

Position Overview

We are searching for the best talent for Research Intern AI/ML & DH to be in Cambridge, Massachusetts.

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

Purpose

Data Science and Digital Health (DSDH) function within Johnson & Johnson Innovative Medicine Research & Development is seeking a PhD‑level Research Intern to support the development of foundation models for multimodal wearable sensor data, including accelerometer, PPG, ECG, and other physiological signals. The intern will work on self‑supervised and multimodal representation learning to build scalable models that generalize across digital health applications such as human activity recognition, sleep staging, cardiovascular monitoring, gait analysis, and digital biomarker discovery.

You will be responsible

• Train foundation models on large‑scale unlabelled multimodal sensor datasets.
• Develop architectures using transformers, contrastive learning, masked modelling, and cross‑modal attention.
• Evaluate learned representations on downstream health tasks (HAR, sleep, stress, gait, health outcomes).
• Contribute to reproducible research outputs, including publications and well‑documented code.

Qualifications / Requirements

• Completion of Undergraduate Freshman year at an accredited University is required.
• Currently pursuing a PhD in Machine Learning, AI, Computer Science, Biomedical Engineering, Signal Processing, or related fields.
• Have a cumulative GPA of 2.8 or higher.
• Strong programming experience with Python and deep learning frameworks (PyTorch or TensorFlow).
• Strong technical background in deep learning for time‑series data and self‑supervised learning.
• Experience working with large‑scale datasets and research pipelines.

Preferred Qualifications

• Experience with wearable sensor data and multimodal machine learning.
• Familiarity with transformers, contrastive learning, and masked modelling.
• Background in physiological signal processing and digital health applications.
• Exposure to time‑series foundation models or large‑scale model training.

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 require 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, depending on candidate’s program year, discipline, degree, and/or experience. Co‑Ops/Interns are eligible to participate in Company‑sponsored employee medical benefits and in the Company’s consolidated retirement plan. Sick time benefits: up to 40 hours per calendar year (up to 56 hours for employees in Washington State). This job posting is anticipated to close on 03/29/2026.

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, please contact us via https://www.jnj.com/contact-us/careers or contact AskGS to be directed to your accommodation resource.