(US) Edge AI Architect– CUDA / C++ / Computer Vision

About the Role:

We are looking for a highly motivated and technically proficient Edge AI Engineer with a strong background in Edge AI devices, computer vision algorithms, and AI model deployment in the MedTech domain. This role is pivotal in developing and optimizing AI-powered medical devices that operate at the edge, ensuring performance, reliability, and compliance with healthcare standards.

Key Responsibilities:

• Design, develop, and optimize AI models for deployment on Edge AI devices in medical applications.

• Implement and evaluate computer vision algorithms for real-time video and image analysis.

• Collaborate with cross-functional teams to integrate AI solutions into embedded systems and medical devices.

• Ensure compliance with SaMD classification, regulatory standards, and quality processes.

• Document design specifications, test protocols, and validation reports in accordance with regulatory requirements.

• Communicate technical findings and project updates effectively to stakeholders.

Required Qualifications:

• Bachelor’s or Master’s degree in Computer Engineering, Electrical Engineering, Biomedical Engineering, or related field.

• 8+ years of experience in AI engineering, preferably in medical devices or healthcare technology.

• Strong experience with Edge AI hardware platforms (e.g., NVIDIA Jetson, Google Coral, Intel Movidius).

• Strong experience in CUDA, C++ writing drivers with contract manufactured Edge AI devices with custom GPU configuration

• Proficiency in computer vision frameworks (e.g., OpenCV, TensorFlow, PyTorch) and model optimization tools.

• AI/ML Acumen (Crucial) Required: Strong LLM, RAG, agentic architecture understanding

• Understanding of SaMD regulations, ISO 13485, IEC 62304, and related standards.

• Experience with embedded systems development and real-time processing.

• Excellent verbal and written communication skills.

Nice to Have:

• Experience testing or developing AI systems for surgical applications.

• Familiarity with test data validation for computer vision and video-based AI models.

• Knowledge of risk management processes (ISO 14971) and cybersecurity standards in healthcare.

• Experience with cloud-edge integration and CI/CD pipelines for AI deployment.