Principal Scientist, AI-Native Discovery (Large Molecules) Full-time NEW

Principal Scientist, AI-Native Discovery (Large Molecules)

Johnson & Johnson Innovative Medicine is recruiting for a Principal Scientist, AI-Native Discovery (Large Molecules) to join our In Silico Drug Discovery team. This position will be located in either Spring House, PA (preferred); Cambridge, MA; or San Diego, CA (La Jolla area) and may require some travel as needed. (No remote option.)

We are seeking a creative, highly collaborative, and self-motivated Principal Scientist to join our In Silico Discovery (ISD), In Silico Biologics team. In this role, you will help shape the future of AI-native biologics discovery by integrating advanced AI, agentic workflows, predictive modeling, and computational design into Therapeutic Discovery programs. You will represent In Silico Discovery on cross-functional project teams, integrating broader ISD capabilities and AI-native approaches.

The ideal candidate combines experience influencing cross-functional discovery programs with therapeutic development expertise and a passion for using AI to generate insights, inform experimental strategies, and improve discovery decisions. You will help build new ways of working that integrate human scientific judgment, machine intelligence, experimental data and computational methods, while leveraging capabilities from across ISD and the broader J&J technical community.

This is an opportunity to join a diverse, global community of in silico scientists and contribute directly to the discovery of innovative medicines for patients.

What You Will Do

• Partner closely with teams across In Silico Discovery to connect biologics discovery programs with relevant computational capabilities and AI-enabled approaches.
• Partner with Biologics Discovery and Therapeutic Area teams to apply AI-enabled computational design, analysis, and decision support in Therapeutic Discovery programs.
• Represent In Silico Discovery on cross-functional project teams and apply AI-enabled computational insights to support program strategy and decision-making.
• Apply predictive modeling, machine learning, generative approaches, agentic AI, structural modeling, and computational biology to support experimental strategy and biologic candidate design and optimization.
• Advance AI-native workflows that connect hypothesis generation, computational design, experimental testing, and model refinement to accelerate learning and decision-making.
• Evaluate emerging technologies and contribute scientific recommendations to internal development, acquisition, partnering, licensing, or M&A opportunities.
• Champion the responsible and scientifically rigorous adoption of AI and contribute to an innovative culture that values collaboration, experimentation, continuous learning, and knowledge sharing.
• Maintain awareness of advances in AI, computational biology, structural modeling, and biologics design.
• Contribute to Johnson & Johnson's external scientific presence through peer-reviewed publications, conference participation, and collaboration with external organizations.

How You Will Make an Impact

You will help define how AI becomes an integral part of biologics discovery, enabling new approaches to scientific learning and decision-making.

By connecting human expertise, AI models, computational design, experimental workflows, and high-quality data, you will help create a more predictive, scalable, and adaptive discovery environment. Your work will contribute to faster learning cycles, stronger design decisions, and the advancement of differentiated biologic medicines for patients.

Why Join Us

• Shape AI-native ways of working across biologics discovery.
• Apply emerging AI capabilities to meaningful therapeutic challenges.
• Work across computational science, experimental biology, data science, and drug development.

Required Qualifications

• PhD in a relevant scientific or technical discipline and a minimum of 4 years of relevant drug discovery experience, including experience contributing to cross-functional discovery teams.
• Knowledge of biologics discovery, candidate optimization, and therapeutic development processes.
• Strong scientific leadership, communication, and stakeholder management skills, including the ability to organize and communicate complex data, models, and scientific concepts clearly to partners with diverse technical backgrounds.
• Demonstrated expertise in AI, machine learning, agentic AI, structural modeling, computational biology, or computational approaches for antibody design and optimization.
• Experience representing a computational function on cross-functional projects and integrating computational and experimental data to inform program strategy and scientific decisions.
• Hands-on experience with large-scale scientific data curation, aggregation, manipulation, integration, mining, and analysis across structured and unstructured data sources.

Preferred Qualifications

• Experience in therapeutic target analysis, structural biology, antibody-antigen interaction modeling, computational protein design, and scientific data visualization.
• Experience with multimodal biological data, generative modeling, foundation models, AI agents, or iterative design-build-test-learn workflows.
• Experience developing, scaling, or adopting computational platforms and workflows in partnership with technology development teams.
• A strong publication record and demonstrated contributions to the discovery or advancement of preclinical or clinical candidates.
• Experience collaborating with academic institutions, biotechnology companies, technology partners, or contract research organizations.
• Experience evaluating emerging computational technologies or external scientific innovation opportunities.
• Experience influencing multidisciplinary discovery teams spanning computational and experimental functions.

Influence both portfolio programs and the evolution of enterprise discovery capabilities.

Contribute to a collaborative global scientific community focused on delivering innovative medicines to patients.