Director, Computer-Directed Drug Design and AI-Native Molecular Design - San Diego, CA

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Johnson & Johnson Innovative Medicine is recruiting for a Director, Computer-Directed Drug Design and AI-Native Molecular Design to join our In Silico Drug Discovery team.  This position will be located in San Diego, CA (La Jolla area) and may require up to approximately 20% travel as needed (no remote option). 

We are seeking a Director to lead a portfolio-embedded Computer-Directed Drug Design (CDDD, CD3) team and advance AI-native scientific discovery across small-molecule therapeutic programs. The successful candidate will bring deep expertise in computational drug discovery and molecular design, while operating with sufficient breadth to connect medicinal chemistry, biology, structural sciences, experimental data, developability, and therapeutic strategy. This leader will co-own program and molecular design strategy, translate project goals into integrated computational and experimental plans, build a high-performing team, and scale repeatable learning systems that improve compound quality and portfolio decisions across traditional Rule-of-Five molecules, beyond-Rule-of-Five modalities, degraders, RIPTACs, molecular glues, peptides, and emerging modalities.

This is a scientific, portfolio-facing leadership role—not a software, or platform development position. The Director will lead drug discovery architects who shape program strategy with medicinal chemistry and cross-functional partners, orchestrate rapid design–make–test–learn cycles, critically evaluate AI-generated proposals, and drive adoption of validated capabilities. The Director will strongly integrate the portfolio-embedded CDDD team with In Silico Discovery partners in Cheminformatics (CI), D3S, ISB (In silico biologics) and CADD Technical Development. Reusable platforms and production-grade capabilities will be built and maintained by CI in partnership with CD3, CADD Technical Development, and D3S; the Director and embedded CD3 team will define scientific use cases, guide prioritization, provide user feedback, support validation, and lead effective application across programs.

• →Co-own program strategy, molecular design priorities, and critical discovery decisions with medicinal chemistry, biology, project, therapeutic area, and developability leaders, with accountability for clear recommendations and measurable portfolio impact.
• →Translate target product profiles, biological hypotheses, project goals, decision points, risks, and constraints into integrated computational and experimental strategies that advance programs from target assessment through candidate nomination.
• →Lead the portfolio-embedded CD3 team as accountable computational molecular architects who combine computational models, experimental evidence, SAR, structural information, synthesis feasibility, safety, intellectual property, resource constraints, predictive properties, and portfolio value to ensure predictive models are used in design, and to guide compound decisions.
• →Orchestrate repeatable design–make–test–learn ways of working by defining scientific questions, decision criteria, feedback loops, escalation paths, knowledge-capture needs, and fit-for-purpose success measures with program teams that can be built into reusable platforms and production-grade capabilities in partnership with your In Silico Discovery teammates.
• →Critically assess AI- and model-generated proposals, including assumptions, applicability, uncertainty, data quality, reproducibility, chemical tractability, and human-review requirements; establish appropriate scientific and responsible-use controls.
• →Strongly integrate the portfolio-embedded CD3 team with ISD partners in CI, D3S, and CADD Technical Development, creating shared priorities, clear interfaces, transparent handoffs, and operating rhythms that translate portfolio needs into reusable solutions across project teams.
• →Define scientifically grounded use cases, capability requirements, acceptance criteria, validation needs, and adoption measures for computational and AI-enabled solutions; provide structured portfolio feedback to the teams accountable for development and lifecycle management.
• →Partner with CI—as the platform-building lead—and with CD3, CADD Technical Development, and D3S to prioritize, validate, deploy, and continuously improve scalable platforms and workflows without duplicating their development accountabilities.
• →Evaluate and select fit-for-purpose computational, AI/ML, generative, agentic, predictive, structural, simulation, cheminformatics, and automation capabilities based on the scientific decision and operational context; guide their responsible use by portfolio teams.
• →Drive adoption of validated AI-native capabilities through practical deployment, change leadership, training, transparent metrics, user feedback, and sustained behavior change.
• →Leverage emerging prompt-driven design-make-test-learn systems to translate program strategy, and to improve AI-generated proposals inspired by project context.
• →Influence program and portfolio governance by communicating evidence, uncertainty, risks, tradeoffs, resource implications, and recommendations clearly to senior leaders and diverse stakeholders.
• →Lead, coach, and develop a high-performing CD3 team; strengthen molecular architect judgment, project leadership, collaborative behaviors, responsible AI use, and AI-native scientific capability across ISD and the organization.
• →Function as an integrator across multidisciplinary contributors across chemistry, biology, structural biology, DMPK, toxicology, CI, D3S, CADD Technical Development, automation, Business Technology, and external partners while maintaining clear decision rights and accountability.
• →Evaluate external technologies and partnerships for scientific validity, strategic fit, scalability, and measurable discovery value; lead scientific collaborations and due diligence as appropriate.
• →Working fluency in AI/ML-enabled discovery, generative molecular design, predictive modeling, protein structure prediction, agentic approaches, AI-assisted synthesis planning, and the scientific controls needed for responsible use.
• →Represent Johnson & Johnson and ISD through scientific collaborations, publications, presentations, consortia, and external engagement, while integrating the Credo and Leadership Imperatives into decisions, talent development, and partnerships.
• →Other duties as may be assigned by the Senior Director, CD3, ISD.

You will shape how computational science and AI become integral to small-molecule discovery—not as standalone analyses or isolated technology projects, but as trusted inputs to continuously improving program decisions. By connecting portfolio-embedded CD3 scientists with medicinal chemistry, experimental partners, automation teams, CI, D3S, ISB, and CADD Technical Development, you will help create faster learning cycles, better compound designs, stronger evidence-based decisions, broader reuse of validated capabilities, and more consistent delivery of differentiated candidates for patients. 

• PhD in computational chemistry, theoretical chemistry, chemical physics, biophysics, cheminformatics, medicinal chemistry, or a related scientific discipline.
• A minimum of 8 years of relevant small-molecule drug-discovery experience, including progressive scientific and people leadership; equivalent combinations of training and experience may be considered.
• Demonstrated impact across multiple discovery programs from hit identification through lead optimization, including contribution to a major progression milestone or candidate nomination.
• Deep applied expertise in computational chemistry, cheminformatics, molecular design, and multiparameter optimization, with the judgment to select methods appropriate to the scientific decision.
• Strong medicinal chemistry and molecular architecture judgment, including the ability to interpret SAR and balance potency, selectivity, pharmacokinetics, safety, synthesis feasibility, intellectual property, resources, and portfolio value.
• Evidence that computational or AI-derived insights changed experimental priorities, compound designs, or program decisions and improved compound quality, learning speed, decision quality, or progression.
• Experience in applying and driving adoption of production-grade computational or AI-enabled capabilities in discovery workflows; direct responsibility for coding, model development, software engineering, or platform construction is not required.
• Proven ability to co-lead in a matrix, influence governance, clarify decision rights, and build effective partnerships across portfolio-embedded scientific teams and specialist capability-development organizations.
• Proven people leader with experience building, coaching, and scaling an application-focused computational drug-discovery team accountable for portfolio impact, collaborative delivery, and practical adoption.
• Executive presence, scientific courage, sound judgment under uncertainty, and the ability to communicate complex evidence, tradeoffs, and recommendations clearly to senior leaders and multidisciplinary partners.

• Experience across multiple small-molecule modalities, such as traditional Rule-of-Five and beyond-Rule-of-Five molecules, degraders, RIPTACs, molecular glues, and peptides.
• Prior accountability as a small-molecule project leader, molecular design lead, or equivalent co-leader of program strategy and progression decisions.
• Experience governing or applying agentic and human-in-the-loop discovery systems in a scientifically rigorous environment.
• Experience translating portfolio use cases into requirements for cheminformatics, AI/ML, technical development, software, data, automation, or technology teams and achieving adoption across multiple programs.
• Experience leading collaborations with academic groups, biotechnology companies, AI platform providers, automation partners, or contract research organizations.
• A strong record of scientific and portfolio impact demonstrated through candidate delivery, patents, publications, presentations, capability adoption, or partnership outcomes.
• Experience leading organizational change, establishing adoption and impact measures, and developing the next generation of AI-native drug-discovery leaders.
• Ability to travel up to 30% domestically and internationally, as needed.