The Position
2026 Summer Intern - Biological Research | AI Development (Multimodal)
Department Summary
We seek a highly motivated research intern to join our Cellular States and their Organizations (CSO) group within Genentech's department of Biology Research | AI Development (BRAID). Our group is committed to designing and implementing transformative machine learning capabilities to model and probe the cellular biology of disease, from DNA variation to tissue organization, and its manifestation in the patient populations we want to impact. These underpin our goal to revolutionize how we discover and characterize novel therapeutic targets and the clinical contexts in which they can be most safe and efficacious. This way, we intend for state-(and future)-of-the-art machine learning to have material and positive societal impact.
In this position, you will drive a project to advance multimodal techniques for the fusion and alignment of biological foundation models in image-based and molecular modalities (e.g., histopathology, spatial transcriptomics). This involves pushing the boundaries of modern tokenization strategies, disentangled representation learning, and explainable AI for the underlying goal of understanding biological mechanisms.
This internship position is located in South San Francisco, on-site.
The Opportunity
In this 12-week program, you will:
Design and develop novel techniques for bridging foundation models across biological modalities and scales.
Apply explainable AI to tease out links between post-hoc explanations and biological signals.
Work closely with an existing team of software developers, machine learning experts, computational biologists, and wet lab collaborators.
Impact key ongoing and planned projects in diverse therapeutic areas including Neuroscience, Immunology, and Molecular Oncology.
Program Highlights
Intensive 12-weeks, full-time (40 hours per week) paid internship.
Program start dates are in May/June (Summer).
A stipend, based on location, will be provided to help alleviate costs associated with the internship.
Ownership of challenging and impactful business-critical projects.
Work with some of the most talented people in the biotechnology industry.
Who You Are
Required Education:
Must be pursuing a PhD (enrolled student).
Required Majors:
Computer Science, Statistics, Mathematics, Computational Biology, Bioinformatics, Bioengineering, or a related field.
Required Skills:
Strong foundation in machine learning, data science, and statistics.
High proficiency in Python and experience with deep learning frameworks (e.g., PyTorch).
Familiarity with basic concepts in biology.
Experience with best practices of software engineering, data engineering, and MLOps (e.g., code version control, high-performance compute infrastructures, and experiment monitoring workflows).
Preferred Knowledge, Skills, and Qualifications
Prior experience with training large scale models or handling large-scale datasets.
Publication record at machine learning conferences or strong biological journals.
Excellent communication, collaboration, and interpersonal skills.
Complements our culture and the standards that guide our daily behavior & decisions: Integrity, Courage, and Passion.
Relocation benefits are not available for this job posting.
The expected salary range for this position based on the primary location of CA is $50.00 hour. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. This position also qualifies for paid holiday time off benefits.
Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.
If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants ( .
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