Senior Scientist - AI Safety

Johnson Johnson; Madrid; Hybrid; EUR 90,000 - 120,000; Full time

Job summary

Johnson & Johnson Innovative Medicine is recruiting a Senior Scientist, AI Safety to join our Data, Data Science & AI organization in Madrid or Barcelona. We operate a hybrid model with three days in the office weekly.

The role centers on governing agentic AI, defining deterministic controls, and building safety-native architectures for regulated pharma R&D. You will work with cross-functional teams and contribute to safety standards and publications.

Qualifications

• Experience in AI safety governance and validation for complex systems.
• Experience with adversarial testing in AI for regulated R&D.
• Ability to translate safety requirements into scalable controls.
• Strong collaboration with cross-functional teams.

Responsibilities

• 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.
• Develop continuous red-teaming methods for agentic AI, combining AI threat models with pharmaceutical R&D failure modes.
• Prototype safety-native architectures and evaluate pre-training/post-training safety interventions.
• Define accountability measures and audit trails for regulatory review; ensure traceability of decisions.
• Collaborate with R&D scientists, platform engineering, and security/privacy/legal/quality functions to guide the safety work.

Skills

AI Safety

Agentic AI

Deterministic controls

Adversarial testing

Cross-functional collaboration

Job 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

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 controls.
• Evaluate pre-training data interventions and post-training methods-including supervised fine-tuning, preference optimization, and safety tuning-for regulated scientific use cases.
• Translate scientific, quality, privacy, and regulatory requirements into testable system specifications for models, agents, tools, and runtime controls.

Evidence, Accountability & Partnership

• Define the accountability bar for this work and the measures that demonstrate it is met, so claims about safety rest on evidence rather than assertion.
• Ensure control decisions are traceable and reconstructable as audit evidence for scientific, quality, and regulatory review.
• Partner with R&D scientists, platform engineering, and the security, privacy, legal, and quality functions; set the scientific direction for a small cross-functional team and represent the work externally through publications and standards contributions.

What This Role Is Not

Not frontier model development. We are not pre-training found