What You'll Do - Lead the research and development of state-of-the-art deep learning models for medical imaging applications
- Design and implement scalable training pipelines for large-scale imaging datasets
- Drive model performance through experimentation, architecture innovation, and rigorous evaluation frameworks
- Translate clinical and scientific requirements into production-ready ML solutions
- Collaborate cross-functionally with data scientists, engineers, and clinical stakeholders
- Mentor and provide technical leadership to ML engineers and data scientists
- Partner with infrastructure teams to optimize GPU utilization, training efficiency, and deployment workflows
- Contribute to scientific publications and support intellectual property development
What You Bring Required - PhD with 4+ years of industry experience, or Master's with 7+ years in Machine Learning, AI, or a related field
- Deep expertise in machine learning and self-supervised learning methods
- Strong programming skills in Python and PyTorch
- Experience building and scaling ML systems in cloud environments (AWS preferred)
- Hands-on experience with medical imaging data (DICOM) and preprocessing pipelines
- Proven track record of developing and deploying ML models in healthcare, biotech, or other regulated industries
- Strong communication skills and ability to collaborate across technical and clinical teams
Preferred - Experience with mammography or breast imaging
- Familiarity with FDA regulatory processes for Software as a Medical Device (SaMD)
- Experience with ML orchestration tools (e.g., Metaflow or similar)
- Expertise in distributed training (multi-GPU/multi-node) and performance optimization
- Publications in leading ML or medical imaging conferences (e.g., NeurIPS, ICML, MICCAI)
- Experience with foundation models, transfer learning, or domain adaptation in medical imaging
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