Senior Machine Learning Scientist, AI for 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

AI Biology & Translation (AIBT) develops and applies state-of-the-art artificial intelligence to accelerate biomedical discovery across Genentech Research and Early Development. We combine advances in foundation models, multimodal machine learning, and large-scale biological data to advance target discovery, disease understanding, biomarker development, and translational science. We are seeking a Senior Machine Learning Scientist with deep expertise in modern machine learning to lead the development and application of next-generation AI technologies for biomedical research. This role is ideal for an accomplished scientist who combines strong technical depth with scientific curiosity and enjoys collaborating across computational and experimental disciplines. You will lead high-impact technical initiatives, develop innovative AI solutions, and help shape the future application of machine learning across biological research.

In this role, you will:

• Develop and apply state-of-the-art machine learning methods to solve challenging problems in biology and translational research.
• Develop, adapt, and evaluate foundation models, large language models (LLMs), multimodal models, and generative AI approaches for biomedical applications.
• Design scalable machine learning workflows that integrate diverse biological, molecular, imaging, and clinical datasets.
• Lead technical efforts to optimize, benchmark, validate, and interpret AI models for scientific use.
• Collaborate closely with computational scientists, software engineers, biologists, clinicians, and therapeutic area researchers to translate cutting-edge AI into impactful scientific capabilities.
• Drive technical excellence by establishing best practices for model development, evaluation, reproducibility, and deployment.
• Stay at the forefront of advances in machine learning and identify opportunities to apply emerging AI technologies to biomedical discovery.

• Ph.D. in Machine Learning, Computer Science, Artificial Intelligence, Computational Biology, Statistics, or a related quantitative discipline with significant postdoctoral and/or industry experience.
• Demonstrated expertise in deep learning, foundation models, generative AI, large language models, multimodal learning, or related areas.
• Proven experience leading technically complex machine learning projects from research through deployment or scientific application.
• Strong programming skills in Python and experience with modern ML frameworks such as PyTorch, JAX, TensorFlow, and Hugging Face.
• Experience adapting, fine-tuning, evaluating, and deploying large-scale AI models.
• Experience developing reproducible and scalable machine learning pipelines and software.
• Excellent communication skills and demonstrated ability to collaborate effectively across multidisciplinary scientific teams.
• A strong interest in applying cutting-edge AI to advance biology, translational science, and drug discovery.

Preferred

• Publications or significant technical contributions in machine learning, artificial intelligence, computational biology, or related disciplines.
• Experience with multimodal biomedical datasets, including genomics, transcriptomics, proteomics, imaging, pathology, or clinical data.
• Experience building production-quality AI systems or scalable machine learning infrastructure.