Job Description

• Lead the design, training, and deployment of machine learning models for wearable imaging and sensing systems — from research prototypes through production.
• Develop computer vision models for image processing and analysis across multiple imaging modalities and sensors.
• Analyze medical imaging data with deep learning models to support diagnostics and clinical decision-making.
• Build training pipelines, data labeling workflows, and evaluation frameworks that scale across imaging datasets.
• Integrate and optimize AI inference into the VETi platform’s real-time imaging and device-control stack, deploying to NPUs, GPUs, and embedded accelerators within latency, power, and memory budgets.
• Debug model accuracy and performance across training, evaluation, and on-device environments.
• Collaborate with electrical, optics, clinical, software, and systems engineering teams.

• B.S., M.S., or Ph.D. in AI, Computer Science, Electrical Engineering, Applied Mathematics, Physics, or a related technical discipline, or equivalent practical experience.
• 5+ years of professional machine learning experience developing and deploying production models.
• Strong programming experience in Python and C++, with at least one major ML framework (PyTorch, TensorFlow, or JAX) and solid software engineering practices.
• Strong deep learning experience, particularly in computer vision (CNNs, transformers, segmentation, detection, classification).
• Experience deploying models to edge devices, including model optimization (quantization, pruning, distillation) and inference runtimes (ONNX, TensorRT, or similar).
• Ability to collaborate across machine learning, software, hardware, scientific, and engineering teams.

Additional Experience That Would Be Valuable

• Experience with medical imaging data (OCT, fundus, retinal scans, MRI, CT, or similar).
• Experience working with 3D or volumetric imaging data (e.g., OCT B-scans, volumetric MRI/CT).
• Experience building AI agents for user interaction or human-in-the-loop systems.
• Experience with self-supervised learning, transfer learning, or other data-efficient methods for limited labeled data.
• Familiarity with regulated medical-device AI development (FDA SaMD, IEC 62304, ISO 13485), or willingness to learn.