Johnson & Johnson
Pharma · Pharma + medtech
- HQ
- New Brunswick, US
- Founded
- 1886
- Size
- 130,000+
- Website
- jjmt.com.tw
Pharma · Pharma + medtech
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 |
| Senior Scientist, AI Applications - R&D Immunology Data Science & Digital Health Senior Scientist role focused on developing and delivering AI/ML scientific applications in Immunology R&D, specifically working with LLMs and advanced data science techniques to accelerate drug discovery. Key focus areas include multimodal reasoning, agent robustness, memory architectures, and uncertainty quantification. The role involves hands-on technical delivery, translating business opportunities into research plans, and contributing to the broader AI strategy. |
| Agent |
| 9 |
| Senior Scientist, AI Applications - R&D Oncology Senior Scientist role focused on developing and delivering AI/ML scientific applications for oncology drug discovery and development. The role involves working with LLMs, multimodal reasoning, agent memory architectures, and uncertainty quantification to accelerate research. Key responsibilities include hands-on technical delivery, prototyping, evaluation, and defining best practices for AI applications. | Agent | 9 |
| 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 |
| Post Doctoral Researcher, Evaluation & Standards in Clinical Operations Postdoctoral researcher to develop evaluation frameworks, rubrics, and quality systems for generative AI agents used in clinical operations and global development workflows. The role involves defining quality dimensions, designing human evaluation protocols, building benchmark datasets, and partnering with AI engineering teams in a regulated pharmaceutical environment. | Eval GateAgent | 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 |
| Senior Scientist Omics - IMM Senior Scientist role focused on analyzing large-scale clinical, genetic, omics, and cell phenotype data to identify and evaluate drug targets for the Immunology therapeutic area. The role involves constructing analytic frameworks, applying statistical modeling and AI/ML methods, and using NLP to annotate targets and biomarkers. This is a research-focused position within healthcare, utilizing AI/ML for drug discovery and development. | Data | 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 |
| Director, Data Science and Digital Health - Hematology, Oncology Director level role focused on applying Data Science and AI/ML to support early-stage Hematology Oncology drug development at Johnson & Johnson. Responsibilities include identifying and delivering Data Science capabilities, applying advanced analytic methodologies to high-dimensional data like real-world evidence, and contributing to data-driven decision-making in R&D. | Data | 7 |