Principal Vector Data Engineer

This job is with Johnson & Johnson, an inclusive employer and a member of myGwork – the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly.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 https://www.jnj.com Job Function: Data Analytics & Computational SciencesJob SubFunction: Data ScienceJob Category: Scientific/TechnologyAll Job Posting Locations: Cornellà de Llobregat, Barcelona, Spain, Madrid, SpainJob Description: Johnson and Johnson Innovative Medicine (J&J IM), a pharmaceutical company of Johnson &Johnsonis recruiting for aVector Data Engineer.This position has a primary location ofBarcelona, Spain.The secondarylocation is Madrid. This is a hybrid role. Ourexpertisein Innovative Medicine is informed and inspired by patients, whose insights fuel our science-based advancements. Visionaries like you workin teams that save lives by developing the medicines of tomorrow. Join us in developing treatments, finding cures, and pioneering the path from lab to life while championing patients every step of the way. Learn more athttps://www.jnj.com/innovative-medicinePosition Summary: The Principal Vector Data Engineer is a technical and strategic leader operating at the intersection of AI, digital health, and therapeutic R&D. This role leads the development of multimodal vector embedding pipelines and foundation model architectures supporting longitudinal data integration, disease progression modeling, and digital biomarker discovery across Neuroscience, Oncology, and Immunology. The successful candidate will guide enterprise-scale vectorization efforts while ensuring compliance with clinical, regulatory, and GxP data standards.Key Responsibilities:Technical Leadership • Lead the design, development, and optimization of vector embedding models for diverse biomedical modalities including clinical, regulatory, imaging (MRI, PET), and digital health data. • Architect scalable, compliant embedding pipelines using modern vector database technologies (FAISS, Pinecone, Weaviate, Milvus, Chroma, etc.). • Establish robust quality-control frameworks for mobile-captured images and convert pixel-level data into high-fidelity vector representations. • Drive the adaptation of state-of-the-art academic methods into production-ready, GxP-aware foundation models. • Oversee multimodal data integration efforts to enable semantic search, retrieval-augmented analysis, and clinical insight generation.Cross-Functional & Regulatory Leadership • Collaborate with data scientists, clinicians, engineering teams, and regulatory/QA partners to ensure models and data pipelines align with GxP, clinical governance, and documentation standards. • Contribute to digital biomarker discovery and predictive modeling for neurodegenerative, neuropsychiatric, oncologic, and immunologic conditions. • Mentor junior engineers and contribute to technical roadmap planning, architectural reviews, and AI strategy development.Qualifications: • MS/PhD in Computer Science, Electrical Engineering, Biomedical Engineering, or related discipline.• 3+ years of experience in multimodal ML, vector representation learning, biomedical signal processing, or large-scale embedding systems.• Expertise in Python, PyTorch/TensorFlow, Hugging Face, and multimodal embedding architectures (CLIP, MedCLIP, BioBERT, TimeSformer, etc.).• Hands-on experience with vector indexing/search systems (FAISS, Pinecone, Weaviate, Milvus, Odrant, Chroma).• Familiarity with sentence-transformers, LangChain, or LlamaIndex for semantic search and RAG workflows.• Understanding of clinical trial data structures, longitudinal monitoring, GxP system requirements, and compliant data lifecycle management.Strategic Impact: • Enterprise biomedical data transformed into vectorized, interoperable assets powering scientific AI and semantic intelligence. • Improved data governance, lineage, and GxP alignment across foundation models and vector pipelines. • Accelerated discovery of digital biomarkers and predictive patterns across therapeutic areas. • Scalable vector infrastructure enabling next-generation clinical and translational AI research.#JRDDS#JNJDataScienceRequired Skills:Preferred Skills: