2027 Spring Intern - OpRegen Machine Learning

OpRegen is an allogeneic hESC-derived cell therapy in development for geographic atrophy (advanced dry AMD), manufactured through hESC expansion, RPE differentiation, and RPE expansion. The OpRegen ML team develops machine learning tools to capture cell morphology from routine microscopy images, non-invasively and at scale, helping understand the biology behind the manufacturing process and providing objective, quantitative metrics.

About the role

This internship centers on our in-house morphology capability, Cellestial. The core opportunity is to turn generated imaging data into insight by running our models across images, exploring results for correlations with phenotypic properties, and extending the capability further. The intern will work closely with our computational team and wet-lab, Process Development, and gRED scientists who generate the data and act on the results.

The internship offers flexibility to shape specifics around the intern's strengths and interests, anchored to the core deliverable of analyzing already obtained images. Key focus areas may include:

• Data analysis and insight: exploring imaging data for correlations between morphological features and phenotypic properties, and reporting findings.
• Model deployment and automation: helping automate inference on new data using our compute infrastructure.
• Visualization and tooling: building tools to explore results and connect them back to experimental context.
• Translation to the process: working with lab, Process Development, and manufacturing partners to identify key metrics.
• Applied research: collaborating with research scientists on questions the morphology data can help answer.
• Data annotation and modeling: annotating additional imaging data and contributing to related model work.

Responsibilities

• Analyze imaging data to uncover correlations between morphological features and phenotypic properties.
• Automate model inference on new data using our compute infrastructure.
• Develop tools for visualizing and exploring results in experimental context.
• Collaborate with wet-lab, Process Development, and manufacturing teams to identify impactful metrics.
• Contribute to data annotation and model improvement efforts.

Requirements

• Pursuing or attained an Associate's Degree, Bachelor's Degree, or Master's Degree (enrolled student or degree attained).
• Major in a quantitative or engineering discipline (e.g., computer science, data science, biomedical engineering, computational biology, or physics).
• Solid understanding of machine learning and deep learning fundamentals, with substantial hands-on experience.
• Strong Python skills; comfortable working in a complex, mature codebase and shared Git repository.
• Genuine strength in at least one of: computer vision, statistics and data analysis, software engineering and MLOps, or data visualization and tooling.
• Curious, coachable, and able to work independently with some ambiguity.

Preferred Qualifications

• Excellent communication, collaboration, and interpersonal skills.
• Experience with image analysis, PyTorch, or high-performance computing (SLURM).
• Alignment with Genentech’s culture and values: Integrity, Courage, and Passion.

Pay

The expected salary range for this position in California is $26.00–$50.00 per hour, based on experience, qualifications, and geographic location.

Schedule

• Intensive 6-month, full-time (40 hours per week) paid internship.
• Program start date: January 2027 (Spring).
• Includes paid holiday time off benefits.

Benefits

• A stipend to help alleviate costs associated with the internship.
• Ownership of challenging and impactful business-critical projects.
• Opportunity to work with leading talent in the biotechnology industry.

This is an on-site position located in South San Francisco. Relocation benefits are not available.