Senior Principal Scientist, Applied AI & Agentic Systems for Small-Molecule Drug Discovery Full-time NEW

Scientific Leader For Ai And Agentic Systems

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.

This is a Boston based, hybrid position requiring 3 days/week onsite.

Key Duties And Responsibilities:

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.

Knowledge And Skills

• 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 responsible AI practices in research environments.
• Ability to balance scientific impact, user experience, computational cost, and operational complexity when designing and deploying AI-enabled workflows.
• Strong product-thinking skills, including the ability to translate scientific challenges into scalable capabilities that deliver measurable value.
• Understanding of the scientific workflows and data modalities associated with protein therapeutics, biologics, or other emerging therapeutic modalities is strongly preferred.
• Excellent communication, collaboration, and stakeholder-management skills, with the ability to influence scientists, engineers, data scientists, and senior leaders across a matrixed organization.

Education And Experience

• Ph.D. in Computational Chemistry, Chemistry, Computer Science, Data Science, Engineering or a related scientific discipline.
• Recognized scientific leader with 8+ years of experience applying computational chemistry, cheminformatics, and related computational approaches to advance small-molecule drug discovery programs and deliver candidate molecules.
• Demonstrated success translating scientific opportunities into scalable computational, data science, software, or AI capabilities that deliver measurable scientific and business impact.
• Significant experience working across discovery research, computational sciences, data science, and engineering organizations.
• Proven ability to lead complex cross-functional initiatives and influence scientific strategy without direct authority.
• Track record of driving adoption of new computational technologies and leading scientific or organizational transformation.
• Experience applying computational, data science, or AI approaches to protein therapeutics, biologics, or other therapeutic modalities is strongly preferred