Key Responsibilities:

• Lead and mentor a team of ML engineers, fostering a culture of innovation and technical excellence.
• Architect and optimize multi-modal deep learning models.
• Oversee the end-to-end ML pipeline, including data preprocessing, model training, evaluation, and deployment.
• Drive the integration of AI models into the HOPPR platform, ensuring seamless interoperability.
• Collaborate with clinicians and regulatory teams to ensure AI models meet medical and compliance standards (e.g., FDA, HIPAA).
• Optimize models for real-world performance, focusing on generalizability, robustness, and explainability.
• Lead initiatives in model interpretability, bias mitigation, and continual learning.
• Scale ML infrastructure and ML Ops best practices for efficient model development and deployment.
• Stay at the forefront of ML advancements and implement cutting-edge techniques in deep learning and medical imaging AI.
• Work closely with cross-functional teams, including product, engineering, and regulatory teams, to align AI solutions with business goals.

Qualifications:

• Recognized industry expert with 15+ years of experience in ML research and engineering, with 8+ years in a leadership role
• PhD in Computer Science, Machine Learning, Biomedical Engineering, or a related field
• Proven ability to drive high-impact research as both a lead author and as a mentor or advisor demonstrated by top-tier publications (NeurIPS, ICML, CVPR, Nature Medicine)
• Demonstrable experience developing and deploying SOTA multi-modal foundation models in healthcare. Extensive hands-on experience with modern deep learning frameworks (PyTorch, TensorFlow) and medical imaging libraries (MONAI, DICOM, ITK)
• Deep expertise in transformer architectures, self-supervised learning, multi-modal fusion, and foundation models
• Proven experience scaling ML research to production systems
• Experience with MLOps, cloud-based ML pipelines, and model deployment in production environments (AWS/GCP/Azure)

Skills:

• Deep knowledge of regulatory requirements for AI in healthcare (FDA, CE, HIPAA, etc.)
• Expertise with large-scale medical imaging datasets and addressing data heterogeneity challenges
• Proven leadership and mentorship experience in building and scaling high-performing research teams
• Exceptional communication skills for translating complex technical concepts to diverse stakeholders
• Passion for advancing healthcare through AI research with vast insight into the unique challenges in medical imaging