Senior/Principal Machine Learning Scientist,...

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 the 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

At Roche’s AI for Drug Discovery (AIDD) group (Prescient Design), we are developing models to drive a step-change in machine learning for drug discovery. We are interested in building models that transform drug discovery from a balkanized process – different models for every drug modality; different models for structure-based hit finding versus ligand-based lead optimization; simulation methods for high-concentration properties that can’t interact with black-box methods for affinity – into a unified one. We are looking for exceptional, experienced machine learning scientists with strong engineering abilities who want to perform high quality research at the intersection of machine learning and biology that have direct impact in therapeutic discovery.

In this role, you will:

As a Senior Scientist:

• Develop novel machine learning methods to answer challenging research questions in large molecule drug discovery

• Work with biological and chemical data from heterogeneous sources

• Contribute to an initiative to consolidate projects in machine learning theory into a single coherent model for lab-in-the-loop drug discovery

As a Principal Scientist:

• Develop novel machine learning methods to answer challenging research questions in large molecule drug discovery

• Work with biological and chemical data from heterogeneous sources

• Lead an initiative to consolidate projects in machine learning theory into a single coherent model for lab-in-the-loop drug discovery

• Significant education in computer science or the life and physical sciences, or equivalent work experience: for example, anything from a BS+7 to PhD+2 years, with experience designing and building machine learning systems, particularly for molecules and biological sequences.

• Demonstrated experience with Python and deep learning libraries such as PyTorch, TensorFlow, or JAX.

• Familiarity with areas of modern machine learning research, such as reinforcement learning, sampling, and multimodal representation learning.

• Demonstrated research experience, including at least one first author publication or equivalent.

• Strong communication and collaboration skills

• Portfolio of computational projects (available on e.g. GitHub)

For a Principal Scientist

• Significant education in computer science or the life and physical sciences, or equivalent work experience: for example, anything from a BS+10 to PhD+5 years, with experience designing and building machine learning systems, particularly for molecules and biological sequences.

• Demonstrated experience with Python and deep learning libraries such as PyTorch, TensorFlow, or JAX.

• Familiarity with areas of modern machine learning research, such as reinforcement learning, sampling, and multimodal representation learning.

• Demonstrated research experience, including at least one first author publication or equivalent.

• Strong communication and collaboration skills

• Portfolio of computational projects (available on e.g. GitHub)