Senior Principal Scientist, Applied AI & Agentic Systems -Molecule Drug Discovery

Vertex is seeking a scientific leader to accelerate the application of AI and agentic systems across drug discovery. Working at the intersection of Computational Drug Design, project teams, Data & Methods, and Digital Technology & Engineering, this individual will leverage deep expertise in computational chemistry, cheminformatics, and AI‑driven drug discovery to identify high‑value opportunities across the design-make-test-analyze (DMTA) cycle and translate them into scientifically rigorous AI co‑scientists that enhance compound design, knowledge synthesis, data interpretation, hypothesis generation, and decision‑making.

Although the primary focus will be small‑molecule drug discovery, the successful candidate will also help extend relevant AI and agentic capabilities to protein therapeutics and other emerging therapeutic modalities. As a scientific leader, product owner, and trusted subject matter expert, the successful candidate will drive the strategy, development, evaluation, deployment, and adoption of AI‑enabled scientific workflows. Success will be measured through broad scientist adoption, improved scientific productivity, faster and higher‑quality decision‑making, reduced time spent on information gathering and analysis, and measurable improvements in DMTA cycle efficiency.

Lead Scientific Transformation Through Applied AI and Agentic Systems Drive the strategic application of AI and agentic systems across small‑molecule drug discovery, while identifying opportunities to extend broadly applicable capabilities to protein therapeutics and other modalities. Partner with project teams and scientific leaders to develop and deploy AI co‑scientists that support knowledge synthesis, SAR analysis, scientific question answering, experiment planning, virtual screening, molecular design, and lead optimization, leveraging established computational chemistry and cheminformatics approaches alongside modern AI methods.

Define scientific evaluation frameworks, benchmarks, and validation strategies to ensure AI systems generate reliable, evidence‑based, and actionable scientific insights. Establish success metrics and drive measurable impact through increased scientific productivity, faster information synthesis, improved decision quality, and more efficient DMTA cycles. Serve as a thought leader and ambassador for AI‑enabled scientific innovation across Discovery Research.

Lead Platform Strategy, Adoption, and Responsible AI Operations Serve as the scientific bridge between Drug Discovery, Data & Methods, and Digital Technology & Engineering, translating scientific needs into scalable AI and agentic capabilities. Provide scientific product leadership for AI‑enabled workflows, ensuring solutions are aligned with discovery priorities and integrated into day‑to‑day scientific practice. Define operational guardrails governing model usage, data access, computational resources, cost management, approval workflows, monitoring, and intervention mechanisms.

Drive adoption through scientist engagement, education, change leadership, and continuous improvement while ensuring compliance with Vertex data governance, cybersecurity, and responsible AI standards.

Deep understanding of small‑molecule drug discovery and the DMTA cycle, with firsthand experience applying computational chemistry and cheminformatics approaches to guide molecular design, lead optimization, and project decision‑making. Expertise in one or more of the following areas: computational chemistry, cheminformatics, molecular design, machine learning, scientific data science, or computational drug discovery with a proven ability to apply these methods to discover, optimize, and advance drug candidates.

Strong understanding of modern AI technologies, including large language models, agentic systems, retrieval‑augmented workflows, and AI‑assisted scientific applications. Demonstrated ability to define scientific evaluation frameworks, benchmarks, and validation strategies for AI‑enabled research tools. Deep knowledge of scientific rigor, data provenance, reproducibility, AI governance, and…