Hybrid Senior Scientist – Generative AI for Content Authoring

at Johnson & Johnson

• Job title: Senior Scientist – Generative AI for Content Authoring
• Job type: Full Time
• Experience level: Senior
• Salary: Not specified
• Degree requirement: Postgraduate Degree
• Tech skills: Python, PyTorch, Tensorflow
• Location requirements: Hybrid, Madrid, Spain

About the role

• Senior Scientist shaping the future of generative AI for content authoring in healthcare at Johnson & Johnson. Working with AI methodologies to develop innovative solutions for regulatory and scientific documentation.

Responsibilities

• Design and develop intelligent agent systems for regulatory and scientific content authoring, including writing, reviewing, editing, and refining documents based on guidelines and user input.
• Advance agent memory architectures and persistent knowledge retention to enable long-horizon reasoning and chain-of-thought grounding.
• Prototype and implement controls for hallucination, uncertainty quantification, and safe output generation.
• Build evaluation and benchmarking frameworks for GenAI-driven content authoring, including automated evaluation suites for prompts, models, and retrieval flows.
• Collaborate with regulatory writers, scientists, engineers, and IT teams to translate emerging AI methodologies into practical, production-ready solutions.
• Serve as an internal domain expert for GenAI Content Authoring specific agents’ systems, driving innovation and protocols across R&D.
• If required, contribute to interpretation and generation of figures and plots, demonstrating multimodal AI experience.

Requirements

• Master's degree in AI/ML, Computer Science, Engineering, Bioinformatics, or related field with 4+ years of hands-on experience in GenAI engineering.
• Hands-on experience with multi-agent frameworks such as LangGraph, DSPy, memO, or similar architectures.
• Strong proficiency in Python and modern deep-learning frameworks (PyTorch, TensorFlow).
• Experience building retrieval-augmented generation (RAG) systems, including chunking, embeddings, vector databases, and reranking.
• Knowledge of advanced mathematical concepts such as uncertainty quantification, probabilistic modelling, or information theory.
• Experience implementing structured generation, schema enforcement, and model/tool orchestration for complex workflows.
• Positive, motivated, collaborative approach with the ability to work independently and within a multidisciplinary environment.

Benefits

• Health insurance
• Professional development opportunities