Lead advanced research and production deployment of multimodal medical imaging models that support diagnostic intelligence.

Drive ideation, research, and development of next-generation models across CT, MRI, X-ray, ultrasound, and multimodal fusion architectures
Execute deep academic-level research with novel model design, benchmarking, ablation studies, and scientific validation
Convert research findings into production-ready systems with reliability, scalability, and clinical fit for real diagnostic workflows
Design and manage pipelines for dataset ingestion, cleaning, labeling, augmentation, multimodal alignment, and large-scale imaging data handling
Perform expert model evaluation, error analysis, robustness testing, and optimization for high-performance inference
Maintain high regulatory standards across workflows, including data governance, auditability, and reproducibility for medical AI
Partner with engineering, product, and clinical teams to deliver models that strengthen diagnostic intelligence and clinical decision support

Education: Master’s Degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Biomedical Engineering, or a closely related technical field
ML frameworks: Expert proficiency in PyTorch and TensorFlow
Model and medical AI tooling: Experience with Ultralytics YOLO, MONAI, and the Hugging Face ecosystem
Medical imaging experience: Hands-on work with medical imaging datasets, DICOM workflows, PACS systems, and radiology modalities
Dataset curation/annotation: Hands-on use of FiftyOne, Roboflow, Labelbox, V7, or Encord
Research impact: Proven ability to lead research initiatives and publish or present advanced imaging work at academic or industry venues
Modeling background: Strong foundation in segmentation, detection, multimodal architectures, and imaging-specific preprocessing pipelines
Deployment: Experience deploying models using Core ML, ONNX Runtime, NVIDIA TensorRT/DeepStream, or major cloud ML platforms (Azure, AWS, GCP)
Engineering foundation: Python, GPU computing, Docker/Kubernetes, and scalable ML systems
Regulatory awareness: Familiarity with medical AI regulatory frameworks, including data privacy, auditability, and reproducibility standards

Experience collaborating with radiologists, imaging scientists, or clinical imaging teams
Experience building multimodal (vision + text) diagnostic or triage models
Experience working in a start-up environment and small teams on big ideas for global impact

Senior Data Scientist - AI Engineer

Job Description