2027 Future Talent Program - AI/ML Biomedical Imaging and Computational Pathology - Intern

Merck • Cambridge, MA

11d • $39,908 - $111,111 • Hybrid

About The Position

The Future Talent Program within the Research & Development Division’s Data, AI, and Genome Sciences (DAGS) Department offers internships lasting 10–12 weeks. The AI/ML imaging group is seeking a highly motivated student with computational skills for a project intended to support skill development and help the student evaluate the company as a potential long-term fit.

Within DAGS, the AI/ML team develops AI methods to accelerate drug target and biomarker discovery. The intern will contribute to next-generation computational approaches for biomedical imaging and computational pathology. The project includes developing foundation model-based methods for histopathology images and spatial omics, characterizing model representations with biologically grounded features, and integrating image-derived features with patient outcome and molecular data to support biological hypothesis generation and biomarker discovery. The intern will collaborate with colleagues in data science, computational pathology, biology, and translational research.

This is a fixed-term internship. The intern must be available to work full-time for 9–12 weeks beginning in May or June 2027. The work arrangement is hybrid. No relocation or travel is required, and visa sponsorship is not available.

Requirements

• Currently enrolled in a PhD program in computer science, AI/ML, computational biology, biomedical engineering, bioinformatics, statistics, or a related field.
• Available to work full-time for 9–12 weeks beginning in May or June 2027.
• Proficiency with Python programming and modern deep learning frameworks such as PyTorch.
• Strong communication and interpersonal skills, and ability to work well in a collaborative team environment.

Nice To Haves

• Familiarity with one or more of computer vision, natural language processing, representation learning, foundation models, interpretable/explainable AI, and generative AI.
• Experience with biomedical or scientific data types, such as medical imaging, genomics, spatial omics, or clinical datasets.
• Track record of scientific communication, such as publications or conference presentations.

Responsibilities

• Develop and apply foundation model-based AI methods to extract biologically meaningful and generalizable representations from histopathology images and spatial omics.
• Design interpretable computational approaches to characterize model representations using biologically grounded features, such as morphological patterns and tumor microenvironment architecture.
• Integrate image-derived features with patient outcome and molecular data to support biological hypothesis generation and biomarker discovery.
• Collaborate with a multidisciplinary team spanning data science, computational pathology, biology, and translational research.

What This Job Offers

• Job Type: Full-time
• Career Level: Intern
• Education Level: Ph.D. or professional degree