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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.

Role

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.;

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 foundation models at scale. The research question is how alignment and architecture should be adapted so that the models and platforms available to us are safe for pharmaceutical R&D.;
- Not a replacement for existing controls. Identity, access management, classification, lineage, and source-system controls remain owned by their teams. This work composes on top of them rather than re-implementing them.
- Not a compliance or audit function. You build the mechanisms that produce auditable evidence; you are not the second-line reviewer who sig

📌 Senior Scientist (Madrid)
🏢 Johnson & Johnson Innovative Medicine
📍 Madrid