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 theagentic AIco-scientist project.Through matrixed leadership,leada cross-functional team across scientific, computational, and technical areas to definepriorities, translateVertex scientific needs into a sequenced roadmap, and deliver scalable agentic AIcapabilities.Ensurethe initiativeremainsaligned topriority scientificneedsacross projects, research sites,andmodalities, with clear goals, decision rights, dependencies, risks, and outcomes.
• Drive scientificandtechnical leadership for internal development of co-scientist capabilities, including development of scientific systemskills, integrating existing scientificdatasets and methods,anddefining 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 standardsand governanceframework.Define acceptance criteria and ongoing evaluation approaches forcorrectness, relevance, novelty, reliability, reproducibility, provenance, uncertainty, usability, andmeasurableimpact onscientificdecision-making.
• Drivedevelopment and deployment across scientific domains and modalities. Partner withscientificleadersand domain expertstoidentifyhigh-valueuse cases, translatescientific workflows into agentic AI opportunities, and guide delivery from prototypetosupported adoptionwhile preservinghuman scientific judgment and accountability.
• Representthe co-scientist effort with executive and senior leader stakeholders, providing clear communication on strategy, tradeoffs, progress, risks, resource needs, and delivery milestones, withappropriate decisionescalation as needed.
• Shape agentic AI workflows and scientific operating models. Definehow agents, models, tools, data, literature, compute, and expert reviewwork together to support complex research questionswithappropriateplanning, traceability,escalation,andreproducibility.
• Influence data, platform, and infrastructure strategy.Partner withinfrastructureand platform leaders to definefoundationalrequirements for trusteddataaccess, knowledge management, model and tool integration,scalable compute, and reliable expansionacross 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 innovationthroughtimelyknowledge ofemergingtechnologies, advancements, and challenges in thefield of scientificagentic AI,andleveragethese external insights todrive a best-in-class AI co-scientisttool

Knowledge and Skills:

• Deepunderstanding ofdrugdiscoveryand scientific researchworkflows andhowshared AI capabilitiescan addressdomain-specific needs across functions, therapeutic areas, and modalities.
• Deepexpertisein modern AI, including LLMs, agentic systems, scientific foundation models, retrieval, tool use, planning, and evaluation.
• Abilityto define rigorous evaluations, benchmarks, validation strategies, and success measures for AI-enabledscientifictools and workflows.
• Exceptional ability to partner effectively withinfrastructure and platform collaborators, throughplatform architecture, tool integration, scalablecompute, and reproducible workflows.
• Exceptional ability to partner effectively with scientific collaborators, through stronggrasp of scientificneedsin drug discovery and research, scientificdata strategy,andstrategic 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, andappropriate humanoversight.
• Demonstrated ability and willingness to teach,engageand support others as they learnnew technologiesand concepts
• Enthusiasm for and the ability to quickly learnnew technologiesand tackle difficult problems

Education and Experience

• Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Computational Biology, Computational Chemistry, Bioinformatics, Engineering, Applied Mathematics,with10+ years of relevantexperiencein scientific drug discovery and research, or comparable training and experience.
• Seniorscientific and technical leadershipexperiencein drug discovery, life sciences, AI/ML, computational science,or a closely relatedfield.
• Track recordleading complex, cross-functional initiatives from strategy through delivery, adoption, and measurable impact.
• Experiencedeveloping, evaluating, deploying, or governing advanced AI, machine learning, computational, or data-driven systems ina scientifically rigorous environment.
• Demonstratedability to influence senior stakeholders, alignteamswithout direct authority, andmakedecisions amid scientific,technical, and organizationalambiguity.
• Experience assessing external technologiesorpartnerships andrecommendingbuild, buy, partner, adapt, orintegrateoptionsbased on evidence and strategic value.
• Recognizedscientific impactthroughpublications, patents, deployed capabilities,external presentations,or comparable contributions.

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