# Senior Scientist - AI Safety at Johnson & Johnson

AI Level 4, AI centrality 92 out of 100. Madrid, Spain.

## Details

- Company: [Johnson & Johnson](https://jobsbylevel.com/companies/johnson-johnson)
- AI level: AI Level 4 (score 92 out of 100)
- Location: Madrid, Spain
- Salary: €55k-€88k
- Posted: October 7, 2026
- Apply: https://jobsbylevel.com/go/6462bfb7-95e9-4e06-84a7-cad41f9562a3

## Description

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: Barcelona, Spain, Madrid, Spain Job Description: Johnson & Johnson Innovative Medicine is recruiting a Senior Scientist, AI Safety to join our Data, Data Science & AI organization in Madrid or Barcelona. We work in a hybrid work model which means 3 days per week in the office. This is a newly created scientific role, reporting directly to the Scientific Fellow, AI Safety. Agentic AI is becoming central to pharmaceutical R&D—from discovery and translational science to development and regulatory work—where evidence standards are rigorous and errors can ultimately affect patient safety and outcomes. Our GenAI Platform supports that shift across a rapidly expanding population of autonomous workflows. Safety at this scale cannot be retrofitted through checks written into individual applications; it must be a property of how these systems are built. You will define how pharmaceutical R&D agentic AI can be governed through provable controls and continuously tested through adversarial assurance —then use that evidence to shape safety-native AI architectures in which safeguards are designed in from the start. The role spans three connected mandates: Provable controls. Define deterministic, explainable controls that persist throughout agentic workflows. Adversarial assurance. Continuously test safeguards against credible failure scenarios and produce defensible evidence. Safety-native architecture. Investigate and implement pre- and post-training safety alignment and defense-in-depth techniques to make agentic AI safe by construction for regulated pharmaceutical R&D. This is a hands-on scientific role. You will set the technical direction, build the prototypes that prove it, and carry the results into the platform with our engineering partners. In partnership with cross-functional teams, including the Johnson & Johnson Gen AI, Technology and Infosec teams, you will translate safety requirements into scalable controls, assurance, and safety-native AI architectures. Key Responsibilities Controls & Deterministic Enforcement Design machine-readable control policies that govern agent actions, tool use, data access, and information flow across agentic workflows. Implement deterministic policy enforcement for high-impact agent actions, with auditable decisions and defined human-approval paths. Enable domain and system owners to author, test, and maintain controls through accessible policy interfaces. Continuous Adversarial Assurance Develop continuous red-teaming methods for agentic AI, combining established AI threat models with pharmaceutical R&D failure modes. Embed adversarial evaluation into the GenAI Platform to continuously test models, agents, tools, and end-to-end workflows. Define evaluation protocols, adjudication criteria, and evidence thresholds that distinguish demonstrated safety properties from unverified claims. Safety-Native Architecture & Alignment Research Research and prototype safety-native architectures that constrain agent behavior through layered technical

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