Experteer Overview In this role, you will advance AI safety in pharmaceutical Ru0026amp; D by defining provable controls, adversarial assurance, and safety-native architectures for agentic AI. You will work within the Data, Data Science u0026amp; AI organization in Madrid or Barcelona, partnering with cross-functional teams to translate safety requirements into scalable platform safeguards. Your work shapes governance and verification evidence at scale, contributing to safe and trusted AI-enabled workflows. This is an impactful, hands-on scientific position with visibility to regulators and stakeholders, offering a path to influence next-generation AI in life sciences.Compensaciones / Ventajas
- Design machine-readable control policies governing agent actions, data access, and information flow across workflows
- Implement deterministic policy enforcement with auditable decisions and human-approval paths
- Enable owners to author, test, and maintain controls via accessible policy interfaces
- Develop continuous red-teaming methods for agentic AI and integrate them into the GenAI Platform
- Define evaluation protocols and evidence thresholds to distinguish safety properties from unverified claims
- Research safety-native architectures and data interventions across training stages (pre- and post-training)
- Translate regulatory, quality, and privacy requirements into testable system specifications
- Define accountability measures and ensure audit-reconstructable evidence for safety claims




- Collaborate with Ru0026D, platform engineering, and security/privacy/legal/quality teams to shape scientific direction and external contributionsResponsabilidades
- PhD in computer science, AI/ML, applied mathematics, or related field (required)
- At least 1 year of post-PhD research or industry experience in AI/ML, autonomous agents, or security-critical systems
- Deep hands-on expertise in agentic AI, foundation models, retrieval-augmented generation, tool orchestration, memory, planning, multi-agent frameworks, and their failure modes
- Proven expertise in AI safety and alignment (e.G., supervised fine-tuning, preference optimization, safety tuning, adversarial evaluation, interpretability, scalable oversight)
- Experience in policy-as-code and authorization; adversarial ML and AI red teaming; information-flow or data-access control; provenance and lineage; formal specification or verification
- Strong AI engineering skills: prototyping, reproducible experiments, production-ready code
- Excellent written and verbal communication; ability to present safety arguments to scientific, engineering, and executive stakeholders
- Scientific rigor in characterizing model/agent behavior, uncertainty, limitations, and evidence strengthRequisitos principales
- hybrid work model
- annual bonus based on performance
- vacation days
- minimum 12 weeks parental leave
- well-being reimbursement
- insurance plans (variable by location)

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