Machine Learning Scientist/Senior Machine Learning Scientist - Agents for Applied Small Molecule Drug Design, AI for Drug Discovery

Genentech•San Francisco, CA

8h•Remote

About The Position

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. 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.

Requirements

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

Nice To Haves

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.

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.