Intern-AI​/ML in Antibody Developability Models

Recruitment began on February 25, 2026

and the job listing Expires on March 12, 2026

At Johnson & Johnson,we believe health is everything. Our strength in healthcare innovation empowers us to build aworld 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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Job Function:

Job Sub Function:

Non-LDP Intern/Co-Op

Job Category:

All Job Posting Locations:

San Diego, California, United States of America

Job Description:

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.

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

Fueled by innovation at the intersection of biology and technology, we’re developing the next generation of smarter, less invasive, more personalized treatments.

Johnson & Johnson Innovative Medicine is recruiting for a Summer Intern – AI/ML in Antibody Develop ability Models position in the Drug Discovery Data Science, In Silico Discovery (ISD) group in La Jolla, CA.

The Intern term is from June to August, 2026.

Full time requirement (40 hours per week).

Job Description

The Drug Discovery Data Science group within the In Silico Discovery (ISD) organization in La Jolla, CA is seeking a current PhD student for a 10-12 week Summer research internship. We are looking for a knowledgeable, self-motivated, creative, and multi-skilled graduate student with a strong interest in machine learning, data science, computational biology, and biotherapeutics discovery. In this role, the intern will design and implement machine learning frameworks that leverage public protein property datasets alongside internal experimental data to improve predictive models.

Thermostability and related properties will be key areas of focus.

Responsibilities

Antibody thermostability is a key property influencing the develop ability, manufacturability, and shelf-life of therapeutic candidates. The intern will contribute to advancing predictive modeling performance by:

Collecting and curating large public datasets.

Developing and engineering features by integrating sequence embeddings from state-of-the-art protein language models and structure-derived descriptors, as well as benchmarking alternative feature sets.

Developing deep learning models to capture general physicochemical stability patterns.

Applying transfer learning by fine-tuning pretrained models on internal antibody thermostability datasets and rigorously evaluating predictive performance.

Publication is encouraged.

Qualifications

• Completion of Undergraduate Freshman year at an accredited University is required.
• Have a cumulative GPA of 2.8 or higher, which is reflective of all college coursework.

PhD student in computational biology, biophysics, machine learning, data science, or related field.

Demonstrated experience in deep learning with proficiency in Python and PyTorch.

Familiar with protein language models and key structural determinants of protein stability.

Experience with transfer learning or working with pretrained deep learning models preferred.

Familiarity with antibody structure and function is preferred.

Strong communication, problem-solving, and collaboration skills.

Candidates must have authorization to work in the U.S.

The individual should be able to work onsite at our La Jolla campus during the whole internship period.

Permane…