Senior/Principal Machine Learning Scientist, Perturbation Biology, AI Biology & Translation (AIBT)

A healthier future. It’s what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That’s what makes us Roche.

Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche’s Research and Early Development organisations at Genentech (gRED) and Pharma (pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. Seamless data sharing and access to models across gRED and pRED are essential to maximising these opportunities. The new Computational Sciences Center of Excellence (CoE) is a strategic, unified group whose goal is to harness this transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide.

The Opportunity

We are seeking a highly motivated and collaborative Senior/Principal Machine Learning Scientist to join the Perturbation Biology group in the Department of AI for Biology & Translation (AIBT) in Genentech Research and Early Development (gRED). The successful candidate will develop the next generation of machine learning models to derive actionable insights from large-scale high-content perturbation experiments for target and drug discovery. This role requires a deep understanding of machine learning applied to sequencing-based perturbation data, a passion for innovation and inter-disciplinary research, and a commitment to improving healthcare outcomes through cutting-edge technology. We are looking for exceptional researchers with a proven ability to develop and implement research ideas. The candidate is expected to lead high profile projects in collaboration with our therapeutic area leads, and to routinely publish work in top-tier machine learning and scientific venues.

In this role, you will:

• Design and apply predictive machine learning algorithms for lab-in-the-loop perturbation screens for drug and target identification.
• Work on and integrate a variety of different data modalities such as molecular structures, omics data, images, and text.
• Collaborate with interdisciplinary and cross-functional teams including biologists, chemists, data scientists, and other stakeholders.
• Build and scale machine learning techniques to massive datasets and aid in the deployment of novel machine learning algorithms.
• Publish in top-tier machine learning venues and/or scientific journals, present results at internal and external scientific venues, conferences, and workshops.

Do you have experience in AI models?, * Educational Background: PhD degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics) or in the physical or life sciences (e.g., Chemistry, Biology) with a strong quantitative focus

• Experience:
• Senior ML Scientist 0-2 years of experience post PhD, Principal ML Scientist 2-7 years of experience post PhD
• Proven track record of developing and applying advanced machine learning models in a research or industry setting.
• Demonstrated interest in problems across biology and chemistry as applied to the discovery and development of treatments for disease.
• Technical Skills:
• Proficiency in scientific programming in Python.
• Extensive experience with Machine Learning frameworks and libraries (e.g., PyTorch, JAX, Tensorflow).
• Strong background in statistics, probabilistic modeling and data analysis.
• Soft Skills: Excellent communication, collaboration, and problem-solving skills.
• Publications: Strong publication record and experience contributing to research communities, including conferences like NeurIPS, ICML, ICLR, CVPR, ICCV, etc.

Preferred

• Practical experience in one or more of the following areas:
• Predictive modeling of perturbation datasets to drive experimental design
• Predictive modeling and/or generative modeling on molecules and other chemistry applications
• Multimodal data integration, in particular between multiple measurement modalities and/or clinical patient data