Principal Vector Data Engineer
Role details
Job location
Tech stack
Job description
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., 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., * 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.
Requirements
-
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.
