Qualifications

• Master's or PhD in Machine Learning / Computer Vision required
• Strong understanding of data structures and CV algorithms
• Critical proficiency in C++ for edge deployment
• Solid Python skills for ML experimentation
• 5+ years of experience in deploying computer vision models on resource-constrained devices
• Experience with PyTorch and model optimization for edge AI
• Proven track record of transitioning models from research to production on embedded hardware

Responsibilities

• Design and deploy computer vision models for real-time edge inference
• Optimize models using techniques like quantization and pruning
• Build and manage MLOps pipelines for model validation and performance
• Develop video processing pipelines that merge classical processing with ML techniques
• Establish engineering best practices to reduce technical debt
• Contribute to the architecture of the computer vision stack from research to implementation

Benefits

• Real impact: your code directly aids patient care
• High autonomy in shaping computer vision architecture
• Work at the forefront of AI, edge computing, and healthcare
• Collaborate with a talented, diverse international team
• Leverage a cutting-edge technology stack in embedded AI and robotics
• Contribute to meaningful advancements in remote patient monitoring
• Transparent, mission-driven culture focused on learning