Machine Learning Scientist - Agents for Applied Small Molecule Drug Design

Join the small-molecule team within AI for Drug Discovery (AI4DD), formerly Prescient Design, at Roche and Genentech's Computational Sciences Center of Excellence as a Machine Learning Scientist / Senior Machine Learning Scientist building agents for applied small-molecule drug design. You will develop autonomous, LLM-driven agentic workflows that orchestrate ML models, physics-based methods, and cheminformatics tools to accelerate discovery, working with world-class chemists and structural biologists., * Design, build, and apply agentic workflows and ML models for key challenges in small-molecule drug design.

• Fine-tune foundation models for drug discovery relevant topics using internal and external datasets and tools.
• Optimize agent-derived hypotheses in close collaboration with world-class computational and medicinal chemists and structural biologists.
• Drive scientific impact through publications, open-source releases, and conference talks.
• Collaborate widely with computational and experimental researchers at Roche and with academic partners.

• You are experienced developing LLM-driven agents for scientific workflows and you understand how to orchestrate tools and models reliably.
• You bring strong machine-learning foundations in linear algebra, probability and optimization, with hands-on experience with GNNs, sequence/language models and reinforcement learning.
• You are fluent in Python and modern agentic coding environments such as LangChain, ML frameworks such as PyTorch or JAX, as well as cheminformatics toolkits like RDKit or OpenEye.
• You hold a PhD or equivalent research depth in machine learning, computer science, chemical engineering or a related quantitative field such as physics or statistics.
• You have a record of scientific excellence evidenced by journal and conference publications or a public portfolio of relevant projects (e.g. hosted on GitHub/GitLab)..

Preferred:

• Hands-on experience orchestrating multi-tool or multi-agent scientific pipelines.
• Hand-on experience working along the small molecule drug discovery value chain and an excitement to engage with chemists
• Familiarity with structural biology datasets