Machine Learning Scientist/Senior Machine Learning Scientist, Agents for Applied Small Molecule Drug Design, AIDD

Machine Learning Scientist/Senior Machine Learning Scientist, Agents for Applied Small Molecule Drug Design, AIDD, 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

We are seeking a Machine Learning Scientist / Senior Machine Learning Scientist to create the next generation of small molecule drug discovery systems, powered by AI agents. You will join the small molecule team within Roche/Genentech’s AI for Drug Discovery (AI4DD) organization, formerly Prescient Design, to combine advances in AI with extensive scientific data, resources, and world-class drug discovery experts. You will play a central role in shaping and building these agentic systems from the ground up to push the boundaries of what AI can achieve in the search for new medicines.

In this role, you will:

• Design, build and deploy AI agents to tackle the biggest challenges in small molecule drug discovery.
• Partner with medicinal and computational chemists, structural biologists, and experimental researchers to test and refine agents in real drug discovery scenarios.
• Implement and deploy computational tools for small molecule drug discovery, including external and internal software, and developing new methods.
• Shape the team’s technical direction by identifying high-impact scientific problems and turning ambitious ideas into new initiatives.
• Pursue cutting-edge computational and machine learning research to develop new models, methods, and tools that advance small molecule drug discovery.
• Share your work through scientific publications, open-source code and workflows, and presentations at leading conferences.

Machine Learning, Small Molecule Drugs, Drug Design, Healthcare, Artificial Intelligence (AI), Drug Discovery, Drug Development, Medical Products, Small Molecule Drugs, Small Molecules, * PhD with 0-3 years of relevant experience, or BS/MS with 5+ years of relevant industry experience, in machine learning, computer science, computational chemistry, chemical engineering, or a related quantitative/scientific field such as statistics or physics.

• Strong Python programming and software engineering skills, with experience building, debugging, and maintaining reliable scientific software or AI systems.
• Hands-on experience designing, building, and evaluating AI agents using frameworks such as the Claude Agent SDK, LangGraph, or similar, including tool integration and multi-step workflows for scientific applications.
• Foundational understanding of chemistry and the core concepts of small molecule drug design.
• Evidence of strong technical or scientific contributions through research publications, impactful software, or AI systems you have built.
• Excellent communication and collaboration skills: able to explain technical ideas clearly, listen closely to scientific partners, give and receive constructive feedback, and take ownership of shared outcomes.

Preferred:

• Practical experience designing, training, and evaluating ML models/methods for scientific or molecular applications (e.g. graph neural networks, generative models, etc.). Proficiency with modern ML frameworks (e.g., PyTorch, JAX, TensorFlow, Hugging Face).
• Understanding of core machine learning foundations.
• A public portfolio of relevant projects available online (e.g., on GitHub/GitLab) is a plus.