Scientific Fellow, Agentic AI (AI Co-Scientist Lead)

For this Director-level role, we are seeking a scientific and technical expert to lead our agentic AI co-scientist initiative across our scientific organization. This individual will serve as the scientific lead on the project, defining and influencing the strategy, architecture, evaluation framework, and delivery plan for co-scientist capabilities across multiple scientific use cases and functions. This effort will span both internal development of capabilities and rigorous evaluation of externally available tools. As the senior scientific leader of the effort, this individual will align cross-functional stakeholders, prioritize scientific needs and capabilities, and drive measurable progress, adoption, and delivery of a trusted AI capability that augments scientific reasoning, hypothesis generation, experimental planning, and decision support. 

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

Key Duties & Responsibilities:

• Serve as the senior scientific leader for the agentic AI co-scientist project. Through matrixed leadership, lead a cross-functional team across scientific, computational, and technical areas to define priorities, translate Vertex scientific needs into a sequenced roadmap, and deliver scalable agentic AI capabilities. Ensure the initiative remains aligned to priority scientific needs across projects, research sites, and modalities, with clear goals, decision rights, dependencies, risks, and outcomes. 
• Drive scientific and technical leadership for internal development of co-scientist capabilities, including development of scientific system skills, integrating existing scientific datasets and methods, and defining agentic roles, skills, and orchestration patterns.  
• Lead evaluation of internal and external agentic AI capabilities. Define evidence-based frameworks, benchmarks, and governance to assess commercial, partnership, open-source, and internally developed options and inform build, buy, partner, or integrate decisions. 
• Establish rigorous scientific validation standards and governance framework. Define acceptance criteria and ongoing evaluation approaches for correctness, relevance, novelty, reliability, reproducibility, provenance, uncertainty, usability, and measurable impact on scientific decision-making.  
• Drive development and deployment across scientific domains and modalities. Partner with scientific leaders and domain experts to identify high-value use cases, translate scientific workflows into agentic AI opportunities, and guide delivery from prototype to supported adoption while preserving human scientific judgment and accountability. 
• Represent the co-scientist effort with executive and senior leader stakeholders, providing clear communication on strategy, tradeoffs, progress, risks, resource needs, and delivery milestones, with appropriate decision escalation as needed. 
• Shape agentic AI workflows and scientific operating models. Define how agents, models, tools, data, literature, compute, and expert review work together to support complex research questions with appropriate planning, traceability, escalation, and reproducibility. 
• Influence data, platform, and infrastructure strategy. Partner with infrastructure and platform leaders to define foundational requirements for trusted data access, knowledge management, model and tool integration, scalable compute, and reliable expansion across functions and modalities. 
• Ensure delivery and adoption by driving milestone-based execution, use-case prioritization, change management, stakeholder alignment, and evidence-based decisions on where co-scientist capabilities produce meaningful scientific and organizational value. 
• Drive innovation through timely knowledge of emerging technologies, advancements, and challenges in the field of scientific agentic AI, and leverage these external insights to drive a best-in-class AI co-scientist tool  

Knowledge and Skills:

• Deep understanding of drug discovery and scientific research workflows and how shared AI capabilities can address domain-specific needs across functions, therapeutic areas, and modalities. 
• Deep expertise in modern AI, including LLMs, agentic systems, scientific foundation models, retrieval, tool use, planning, and evaluation. 
• Ability to define rigorous evaluations, benchmarks, validation strategies, and success measures for AI-enabled scientific tools and workflows. 
• Exceptional ability to partner effectively with infrastructure and platform collaborators, through platform architecture, tool integration, scalable compute, and reproducible workflows. 
• Exceptional ability to partner effectively with scientific collaborators, through strong grasp of scientific needs in drug discovery and research, scientific data strategy, and strategic understanding of relative prioritization and impact of different opportunities. 
• Strong strategic judgment, executive communication, stakeholder management, collaboration, and mentoring skills. 
• Excellent presentation, verbal, and written communication skills, including effective communication with senior leaders and cross-functional stakeholders 
• Commitment to scientific rigor, responsible AI, reproducibility, transparency, data governance, cybersecurity, privacy, and appropriate human oversight. 
• Demonstrated ability and willingness to teach, engage and support others as they learn new technologies and concepts 
• Enthusiasm for and the ability to quickly learn new technologies and tackle difficult problems 

Education and Experience 

• Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Computational Biology, Computational Chemistry, Bioinformatics, Engineering, Applied Mathematics, with 10+ years of relevant experience in scientific drug discovery and research, or comparable training and experience. 
• Senior scientific and technical leadership experience in drug discovery, life sciences, AI/ML, computational science, or a closely related field. 
• Track record leading complex, cross-functional initiatives from strategy through delivery, adoption, and measurable impact. 
• Experience developing, evaluating, deploying, or governing advanced AI, machine learning, computational, or data-driven systems in a scientifically rigorous environment. 
• Demonstrated ability to influence senior stakeholders, align teams without direct authority, and make decisions amid scientific, technical, and organizational ambiguity. 
• Experience assessing external technologies or partnerships and recommending build, buy, partner, adapt, or integrate options based on evidence and strategic value. 
• Recognized scientific impact through publications, patents, deployed capabilities, external presentations, or comparable contributions. 

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