Senior Deep Learning Engineer for Medical Computer Vision
We are seeking a skilled Senior Deep Learning Engineer to spearhead the development of our medical computer vision system from architecture through to clinical deployment.
Working closely with the medical founder, who provides expert feature-level annotations, we aim to overcome the common challenge of poor-quality training data.
Key Responsibilities:
* Design, implement, and optimize deep learning computer vision models for dermatoscopic feature segmentation (e.g., U-Net, Mask R-CNN) and final lesion classification (benign vs malignant)
* Develop robust MLOps pipelines for training, validation, deployment, and monitoring
* Lead AI documentation efforts for TGA SaMD compliance, model performance reporting, risk analysis, and data integrity
* Implement Explainable AI methods to support clinician trust
* Collaborate weekly with the clinical founder for validation and iteration
Essential Qualifications:
* 2+ years of hands-on experience in Deep Learning / Machine Learning
* Proven real-world experience with image segmentation (U-Net or similar)
* Academic project experience in image segmentation is acceptable as a minimum level of experience
* Strong proficiency in Python and PyTorch or TensorFlow
* Experience deploying ML systems in cloud environments
* Prior exposure to medical imaging and regulated software environments
Desirable Qualifications:
* MSc or PhD in Computer Vision, AI, or a related discipline
* Experience with multi-task learning
* Strong understanding of clinical performance metrics (AUC, sensitivity, specificity)
* Experience managing data bias and class imbalance in medical datasets