Johnson & Johnson currently has 73 active AI-related job listings. The majority of these roles, specifically 44%, are in the agents stage. Engineering is the most frequent function for these hires, followed by Product. The company is primarily hiring in the United States. Frequent tech tags include agent_orchestration and model_serving, suggesting a focus on AI system deployment and management. In the last 30 days, Johnson & Johnson posted 104 new AI roles, representing a significant increase of 420% compared to the previous 30-day period.
Currently tracking 49 active AI roles, down 53% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $77k–$283k (avg $144k).
Johnson & Johnson currently has 62 active AI-related roles in our index. The most common open titles are: Principal Scientist, Data Science - DDSAI - Therapeutics Development Supply (4), Data Scientist and Application Developer (2), Lead-Data Automation and Excellence (2), Postdoctoral Data Analytics Computational Sciences (2), Senior Program Manager, R&D (2). Most positions are in Engineering and Product.
Johnson & Johnson's active AI hiring is concentrated in: agents (40%), application (19%), data (18%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Johnson & Johnson is hiring AI talent in: United States (38 roles), Spain (7 roles), Belgium (5 roles), Japan (4 roles).
Job postings at Johnson & Johnson most frequently reference: agent orchestration, model serving, llm observability, fine tuning, rag.
In the past 30 days, Johnson & Johnson has posted 69 new AI-related roles. That is a -37% change versus the prior 30 days (109 → 69).
| Title | Stage | AI score |
|---|---|---|
| Postdoctoral Data Analytics Computational Sciences Postdoctoral researcher for application of advanced AI/ML methods to microscopic images of clinical and preclinical histopathology. Focus on application and refinement of deep learning algorithms to quantitate histopathology, and implementation of novel state-of-the-art techniques. Improve understanding of cellular organization and tissue structure to enhance clinical trial efficacy and efficiency. | Post-trainServe | 9 |
| JJT Intern Internship role focused on developing, training, and optimizing ML/DL models (Generative AI, Computer Vision, NLP, Predictive Analytics) within the healthcare domain. Responsibilities include research, data pipelining, model evaluation, and integration into production environments. Requires Master's/Ph.D. in a technical field, strong Python/PyTorch/TensorFlow skills, and a portfolio demonstrating AI/ML model building. |
| Post-trainData |
| 9 |
| Sr Director Head of Data Science Services Senior Director, Head of Data Science Services for HR at Johnson & Johnson. This role leads the enterprise HR data science strategy, focusing on building predictive, diagnostic, and prescriptive models, and industrializing insights. It involves owning the employee listening strategy, knowledge management, and ensuring responsible AI practices, including exploring generative AI and LLMs. The role requires leading a global team and partnering with various stakeholders to embed AI into HR products and workflows. | Post-trainAgent | 8 |
| Postdoctoral Data Analytics Computational Sciences Postdoctoral researcher focused on applying and refining deep learning algorithms for microscopic image analysis in histopathology, aiming to improve drug discovery and clinical trial efficacy. The role involves developing innovative computer vision solutions, collaborating with cross-functional teams, and contributing to scientific publications. | Post-train | 8 |
| Senior Scientist, Multiomic Therapeutics Senior Scientist role focused on computational biology and AI/ML model development for therapeutic discovery, specifically an AI-powered siRNA design and off-target prediction framework. Requires expertise in omics data analysis, AI/ML, and pipeline development. | Post-train | 8 |
| Senior Scientist, Multiomics Perturbation Senior Scientist role focused on applying advanced computational models and AI/ML to integrate multi-modal perturbation data and human omics datasets for target/pathway nomination in therapeutic discovery for immune-mediated diseases. The role involves developing predictive frameworks, identifying target combinations, and performing in silico target deconvolution to inform portfolio decisions. Requires expertise in AI/ML for biological datasets, prediction modeling, phenotype scoring, and multi-omics integration. | Post-train | 8 |
| Principal Scientist, Data Science - DDSAI - Therapeutics Development Supply Seeking a Principal Data Scientist to design, build, validate, and deliver applied AI/ML and statistical modeling solutions for Drug Product Development & Supply (DPDS), focusing on Therapeutics Development & Supply (TDS). The role involves partnering with various teams to create practical, high-impact models supporting CMC development, pharmaceutical product development, dossier preparation, technical development, and clinical supply. Responsibilities include hands-on model development, feature engineering, diagnostics, error analysis, uncertainty assessment, developing reusable workflows using Python and cloud services, applying software engineering best practices, and providing technical guidance. | Post-train | 7 |
| Principal Scientist, Data Science - DDSAI - Therapeutics Development Supply Seeking a Principal Data Scientist to design, build, validate, and deliver applied AI/ML and statistical modeling solutions for Drug Product Development & Supply (DPDS) within Johnson & Johnson Innovative Medicine. The role involves hands-on model development, translating scientific questions into modeling problems, and ensuring model robustness and actionability. It also requires applying software engineering best practices, collaborating with cross-functional teams, and providing technical guidance. Experience in pharmaceutical development or a related quantitative field is preferred. | Post-train | 7 |
| Postdoctoral Scholar, AI/ML for drug metabolite prediction and LC-MS analytical chemistry This role focuses on developing and evaluating AI/ML models for drug metabolite prediction, pharmacokinetics, and molecular properties within a healthcare/drug discovery context. It involves fundamental research using internal datasets, developing explainable AI tools with uncertainty estimation and active learning, and publishing findings. The role is primarily research-oriented, building and evaluating deep learning methods for analytical instrumentation and drug discovery processes. | Post-train | 7 |
| Postdoc on cofolding models for In-Silico Drug Discovery Postdoctoral researcher focused on developing and fine-tuning co-folding models for in-silico drug discovery, involving dataset curation, model evaluation, and integration into discovery workflows within a healthcare/biotech domain. | Post-train | 7 |