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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 in San Diego, CA. This leadership role will co-own program strategy and advance AI-enabled discovery across small-molecule programs.
The Director will integrate computational design with medicinal chemistry, biology, and cross-functional partners, driving repeatable design-make-test-learn cycles and guiding responsible use of
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.
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Data Analytics & Computational Sciences
Data Science
People Leader
San Diego, California, United States of America
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).
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.
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,