About the role#
The OpRegen ML team develops tools to analyze the manufacturing process for OpRegen, an allogeneic cell therapy. We use machine learning to capture cell morphology from microscopy images at scale. This internship focuses on our Cellestial platform, where you will turn imaging data into actionable insights. You will work closely with our computational team, wet-lab scientists, and Process Development partners to identify key metrics and explore phenotypic properties.
What you'll do#
- Analyze imaging data to find correlations between morphological features and phenotypic properties.
- Deploy models and automate inference using our compute infrastructure.
- Build tools to explore results and connect them to experimental contexts.
- Collaborate with research scientists to answer questions using morphology data.
- Annotate imaging data and contribute to ongoing model development.
What you'll need#
- Pursuing or have attained an Associate's, Bachelor's, or Master's degree in Computer Science, Data Science, Biomedical Engineering, Computer Engineering, or Physics.
- Solid understanding of machine learning and deep learning fundamentals.
- Strong Python skills, with experience working in complex codebases and Git repositories.
- Proficiency in at least one area: computer vision, statistics, or data analysis.
- Ability to work independently, curiosity, and a coachable mindset.
Location & details#
- Location: South San Francisco, California.
- Term: Winter 2027 (Spring start).
- Duration: 6 months, full-time (40 hours per week).
- This is an on-site, paid internship.
About Genentech
Founded in 1976 and headquartered in South San Francisco, California, Genentech is a biotechnology research organization with over 10,001 employees. This privately held company focuses on the development of medicines for conditions related to oncology, immunology, neuroscience, infectious disease, metabolism, ophthalmology, and disorders of tissue growth and repair. The firm provides support resources for patients and caregivers while maintaining a research-based approach to addressing complex health challenges.
