2026 Summer Intern - Translational Safety

2026 Summer Intern - Translational Safety

Department Summary

Development Sciences (DevSci) spans the entire drug discovery and development cycle - from early-stage research to drug commercialization. Within DevSci, the Translational Safety (TS) department is responsible for the preclinical safety evaluation of candidate therapeutic molecules. Our mission is to ensure the safety of medicines advancing through the pipeline, supporting the vision of delivering the right drug in the right dose to the right patient.

The Digital Pathology team sits within TS and focuses on revolutionizing the analysis of histopathology slides. We bridge the gap between computational innovation and biological insight. Our objective is to integrate cutting-edge AI techniques directly into pathology workflows, developing computational tools that don't just analyze data but actively support pathologists in identifying and interpreting critical findings. The algorithms you help develop will serve to enhance human decision-making and accelerate the real-world safety assessment of life-saving therapies.

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

The Opportunity

You will join the Digital Pathology group to work closely with computational scientists and pathologists on real-world scientific problems.

The specific focus of this internship is on developing advanced deep learning frameworks for whole slide image (WSI) analysis. You will explore Weakly Supervised Learning (e.g., Multiple Instance Learning) to characterize complex histological patterns and predict safety-related outcomes. This project challenges you to move beyond standard classification tasks by building novel model architectures-leveraging attention mechanisms-to aggregate feature-rich information and identify the specific morphological regions driving the model's predictions.

Key Responsibilities

• Algorithm Development: Conceptualize and implement deep learning-based image analysis algorithms, specifically focusing on MIL and attention mechanisms.

• Data Pipeline Engineering: Handle large-scale whole slide images (WSI), including preprocessing, artifact removal, and patch extraction.

• Scientific Collaboration: Work alongside pathologists to interpret model outputs and validate that computational findings align with biological reality.

• Research & Presentation: Adapt existing research code/papers to internal data and present your findings to the department at the conclusion of the internship.

Program Highlights

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

• Program start dates are in May/June 2026.

• 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 have attained a Master's Degree.

• Must be pursuing a PhD (enrolled student).

Required Majors: Biomedical Engineering, Bioinformatics, Computer Science, Electrical Engineering, Computational Biology, Data Science, Applied Mathematics, Statistics, or a related field.

Required Skills:

• Demonstrated experience working on projects from conceptualization to delivery.

• Demonstrated research experience. Experience reading and adapting research code/papers.

• Proficiency in a general purpose computing language (e.g. C++, Python, javascript). Python is preferred, but not required.

• Interest in the intersection of biomedical sciences and image analysis.

• Enthusiasm to learn about and implement cutting edge AI solutions for computational problems.

Preferred Knowledge, Skills, and Qualifications

• Excellent communication, collaboration, and interpersonal skills.

• Complements our culture and the standards that guide our daily behavior & decisions: Integrity, Courage, and Passion.

• Advanced Deep Learning Architectures: Knowledge of modern computer vision backbones (e.g., ResNet, ViT, ConvNeXt) and a strong conceptual understanding of attention mechanisms (e.g., Self-Attention, Cross-Attention) particularly as applied to feature aggregation and Multiple Instance Learning (MIL).

• Whole Slide Image (WSI) Processing: Experience handling gigapixel-resolution medical images. Familiarity with standard WSI libraries (e.g., OpenSlide, TiffFile, Monai) and preprocessing pipelines (tissue detection, artifact removal, and patch extraction/tiling).

• HPC & Linux Environments: Comfort working within Linux/Unix shell environments (bash scripting) and managing heavy training workloads on High-Performance Computing (HPC) clusters using job schedulers like Slurm.

• Deep Learning Implementation: Practical experience implementing, training, and evaluating models in PyTorch. Familiarity with modern training optimizations (e.g., autocasting, flash attention, distributed data parallel) is a strong plus.

• Biomedical Data Context: Experience working with microscopy or medical imaging data, understanding the nuances of "Many-to-One" data mapping problems.

• Code Quality: Programming proficiency including coding best practices, modular design, and version control (git).

Relocation benefits are not available for this job posting.

The expected salary range for this position based on the primary location of California 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.

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