2026 Summer/Fall Intern – AI/ML for Systems Biology

2026 Summer/Fall Intern – AI/ML for Systems Biology

Department Summary

The AI for Biology & Translation (AIBT) department, housed within the Computational Sciences Center of Excellence, operates at the cutting-edge intersection of artificial intelligence and translational genomics. Our mission is to accelerate drug discovery and deepen the understanding of complex disease mechanisms by bridging the gap between laboratory insights and clinical applications.

Through the development, evaluation, and adaptation of large-scale, cross-species foundational AI models (such as single-cell RNA-seq models), AIBT robustly maps complex cellular states and evolutionarily conserved gene programs between human clinical data and animal disease models. By translating raw biological data into actionable therapeutic targets, the Systems Biology group within AIBT plays a critical role in advancing next-generation medicine from the bench to the bedside.

This internship position is located in South San Francisco, on-site.

The Opportunity

• Benchmark and refine large-scale foundational AI/ML models (e.g., transformer-based architecture) optimized for cross-species cell-type and gene program alignment.

• Leverage machine learning to identify conserved vs. species-specific cellular responses, mapping clinical patient phenotypes directly to preclinical mouse phenotypes.

• Work closely with cross-functional teams, including AI scientists, computational biologists, and disease-area experts to interpret results and develop new lines of research.

• Document and package clean, reproducible pipelines and code. Summarize your findings and present future recommendations in a final oral and poster presentation to the broader AIBT department.

Program Highlights

• Intensive 12-weeks, full-time (40 hours per week) paid internship.

• Program start dates are in August/September.

• 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) 

Required Education:

You meet one of the following criteria:

• Must be pursuing a Master's Degree (enrolled student).

• Must be pursuing a PhD (enrolled student).

Required Majors: Computational Biology, Bioinformatics, Data Science, Computer Science, Statistics, Bioengineering, Systems Biology, or a related highly quantitative field.

Preferred Knowledge, Skills, and Qualifications:

• Strong theoretical and practical foundation in machine learning/deep learning workflows, particularly in architectures relevant to high-dimensional biological data (e.g., Transformers, VAEs, Contrastive Learning).

• Proficiency in Python and familiarity with machine learning frameworks (e.g., PyTorch, TensorFlow) and standard data science toolkits.

• Prior hands-on experience handling and analyzing single-cell genomics data (scRNA-seq).

• 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  California is $40.00-$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.