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

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Learn more at jnj.com . As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit. Job Function: Data Analytics & Computational Sciences Job Sub Function: Data Science Job Category: People Leader All Job Posting Locations: San Diego, California, United States of America Job Description: Our expertise in Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you work on teams that save lives by developing the medicines of tomorrow. Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way. Learn more at https://www.jnj.com/innovative-medicine 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). The Opportunity 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. What You Will Do 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, b