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). #J-18808-Ljbffr Jobtailor