Senior Machine Learning Scientist - Agents for Applied Small Molecule Drug Design, AI for Drug [...]

Responsibilities

• 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.

Requirements

• 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 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 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).