Senior Machine Learning Scientist (AI for Drug Discovery)

TL;DR

Senior Machine Learning Scientist (AI for Drug Discovery): Developing autonomous, LLM-driven agentic workflows that orchestrate ML models and cheminformatics tools for small-molecule drug design with an accent on agentic orchestration and foundation model fine-tuning. Focus on designing multi-tool scientific pipelines and optimizing agent-derived hypotheses to accelerate the discovery of transformative medicines.

Location: South San Francisco, CA or New York City, NY. Relocation benefits are NOT available.

Salary: $141,100 - $310,800

Company

hirify.global, a member of the Roche group, is a biotechnology pioneer dedicated to discovering and developing medicines for people with serious and life-threatening diseases.

What you will do

• Design, build, and apply agentic workflows and ML models for small-molecule drug design challenges.
• Fine-tune foundation models for drug discovery using internal and external datasets and tools.
• Optimize agent-derived hypotheses in close collaboration with computational and medicinal chemists and structural biologists.
• Drive scientific impact through publications, open-source releases, and conference talks.
• Collaborate with computational and experimental researchers at Roche and academic partners.

Requirements

• PhD or equivalent research depth in machine learning, computer science, chemical engineering, physics, or statistics.
• Experience developing LLM-driven agents for scientific workflows and orchestrating tools reliably.
• Strong foundations in linear algebra, probability, and optimization.
• Hands-on experience with GNNs, sequence/language models, and reinforcement learning.
• Fluency in Python, ML frameworks (PyTorch or JAX), and cheminformatics toolkits (RDKit or OpenEye).
• Scientist: up to 2 years of industry research experience; Senior Scientist: 2+ years of industry research experience.

Nice to have

• Hands-on experience orchestrating multi-tool or multi-agent scientific pipelines.
• Experience working along the small molecule drug discovery value chain.
• Familiarity with structural biology datasets.