Experteer Overview In this role you drive safe, trustworthy agentic AI for pharmaceutical Ru0026amp; D within Johnson u0026amp; Johnson’s Data, Data Science u0026amp; AI group in Madrid or Barcelona. You define provable controls, adversarial assurance, and safety-native architectures to ensure AI systems are safe by design for regulated workflows. You will collaborate with cross-functional teams to translate safety requirements into scalable controls and prototypes, shaping the GenAI Platform and contributing to scientific and regulatory proof. This is an impactful, hands-on role at the frontier of AI safety in life sciences.Compensaciones / Incentivos
- Design machine-readable control policies governing agent actions, tool use, data access, and information flow across agentic workflows.
- Implement deterministic policy enforcement with auditable decisions and defined human-approval paths.
- Enable domain and system owners to author, test, and maintain controls via accessible policy interfaces.
- Develop continuous red-teaming methods for agentic AI, integrating threat models with Ru0026D failure modes.
- Embed adversarial evaluation into the GenAI Platform to test models, agents, tools, and end-to-end workflows.
- Define evaluation protocols and evidence thresholds to distinguish safety properties from unverified claims.
- Research and prototype safety-native architectures that constrain agent behavior with layered controls.
- Evaluate pre-training data interventions and post-training methods for regulated scientific use cases.
- Translate scientific, quality, privacy, and regulatory requirements into testable system specifications for models, agents,



tools, and runtime controls.
- Define accountability measures and ensure control decisions are traceable and reconstructable as audit evidence.
- Partner with Ru0026D scientists, platform engineering, security, privacy, legal, and quality teams; guide a small cross-functional team and represent work externally through publications and standards contributions.Responsabilidades
- PhD in computer science, AI/ML, applied mathematics, or closely related field
- At least one year of post-PhD research or industry experience in AI/ML, autonomous-agent, or security-critical systems
- Deep hands-on expertise in agentic AI (foundation models, retrieval-augmented generation, tool orchestration, memory, planning, multi-agent frameworks)
- Experience in AI safety and alignment (supervised fine-tuning, preference optimization, safety tuning, adversarial evaluation, interpretability, scalable oversight)
- Expertise in policy-as-code, authorization, information-flow or data-access control, provenance and lineage, or formal specification/verification
- Strong AI engineering skills (prototyping, reproducible experiments, production-ready code)
- Excellent written and verbal communication; ability to present AI safety arguments to diverse stakeholders
- Scientific rigor in characterizing model/agent behavior, uncertainty, and evidence quality
- Familiarity with safety frameworks and governance concepts (e.G., threat models, formal methods)Requisitos principales
- annual bonus target
- vacation days
- parental leave (minimum 12 weeks)
- bereavement leave
- caregiver leave
- well-being reimbursement

📌 Senior Scientist - Ai Safety (Madrid)
🏢 Johnson u0026 Johnson
📍 Madrid