Principal Scientist, AI & Autonomous Discovery Systems - Spring House, PA

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: Scientific/Technology All Job Posting Locations: Spring House, Pennsylvania, 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 Principal Scientist, AI & Autonomous Discovery Systems, to advance AI-powered drug discovery through human-guided autonomous and agentic discovery platforms and help accelerate drug discovery by creating AI-enabled Design-Make-Test-Learn ecosystems that improve scientific decision quality, increase experimental throughput, reduce cycle times, and advance high-value therapeutic programs. The primary location for this position is Spring House, PA. (No remote option.) The Data, Data Science, and AI team develops AI solutions across therapeutic areas, including oncology, cardiovascular and metabolic disorders, immunology, neuroscience, and infectious disease. We are seeking a technical leader at the intersection of AI/ML, drug discovery, laboratory automation, and scientific platform architecture. This role will help shape the future of AI-enabled research at Johnson & Johnson by creating intelligent systems that connect models, data, experiments, laboratories, and scientists into continuously learning discovery ecosystems. These capabilities will accelerate the discovery and development of transformational medicines across therapeutic areas. Key Responsibilities Build AI-enabled discovery workflows that connect target discovery, molecular/property prediction, molecular design, experiment planning, assay optimization, and data interpretation across modalities, therapeutic areas, and functional groups. Define the technical vision, long-term strategy, and scalable multi-agent architecture for AI-driven laboratory orchestration and human-guided autonomous discovery systems, coordinating scientific reasoning, knowledge retrieval, experiment planning, model execution, workflow automation, and decision support while influencing investments across discovery platforms, therapeutic areas, and enterprise AI initiatives. Connect AI models, scientific applications, data platforms, laboratory systems, instruments, robotics, cloud services into coherent Design-Make-Test-Learn workflows that continuously learn from experimental outcomes, assay results, and scientific feedback. In collaboration with generic agentic system providers, develop intelligent scientific systems for drug discovery and translational research that integrate experimental data, scientific literature, domain knowledge, and predictive multimodal AI models to advance hypothesis generation, experimental design, causal reasoning, active learning, multi-objective optimization, and scientific decision-making. In collaboration with broader teams, contribute to the establishment of rigorous standards for AI governance, safety, observability, reliability, auditability, data governance, security, and responsible AI, and evaluate systems against scientific, operational, and business outcomes through experimental validation and real-world deployment. Partner with scientists and engineers across drug discovery, data science, automation, and technology organizations to deliver production-grade AI capabilities. Mentor scientists and engineers; communicate technical strategy and results through internal presentations, publications, patents, conferences, and collaborations. Qualifications Ph.D. in Biomedical / Electrical / Chemical Engineering, Computer Science, Machine Learning, Computational Biology, Bioinformatics, Computational Chemistry, or a related quantitative discipline. At least 3 years of post-graduate experience applying advanced AI/ML technologies in scientific, pharmaceutical, biotechnology, healthcare, or industrial research settings. Strong expertise in agentic AI, scientific computing, and modern software engineering, machine learning, generative AI, foundation models, multimodal AI, with proficiency in Python and frameworks such as PyTorch, TensorFlow, JAX, or equivalent platforms. Demonstrated ability to architect enterprise-scale AI platforms, scientific software systems, or production-grade AI applications. Experience with drug discovery or clinical development, with ability to partner effectively with domain scientists and experimental teams. Excellent written and verbal communication skills; ability to work independently and collaboratively in a matrixed organization. Preferred Qualifications Experience designing human-guided autonomous or closed-loop scientific workflows that connect active learning, laboratory automation, robotics, scientific data platforms, laboratory information systems, knowledge repositories, cloud infrastructure, and external scientific services. Experience leveraging biological, molecular, multimodal, or scientific foundation model platforms for drug discovery, including model adaptation, fine-tuning, evaluation, and deployment in production scientific workflows. Demonstrated experience defining modular architectures, interoperability standards, governance patterns, and production engineering approaches for enterprise scientific AI platforms, including retrieval-augmented generation, knowledge graphs, MLOps or LLMOps, cloud computing, distributed systems, APIs, and integration across computational and laboratory environments. Experience with analytical instrumentation used in pharmaceutical or process chemistry settings, including LC, LC/MS, and NMR, as well as familiarity with laborator