Computer Vision ML Engineer

What you'll work on

• Design, fine-tune, and deploy computer vision models (YOLO, InsightFace, MediaPipe, facial landmark detection, object tracking, pose estimation) for real-time inference on the edge
• Optimize models for embedded deployment using quantization, pruning, TensorRT, and NVIDIA Triton
• Build and maintain MLOps pipelines for model training, validation, and performance monitoring
• Develop video processing pipelines that integrate with both classical signal processing and ML based vital sign extraction
• Establish engineering best practices and help reduce technical debt as we scale
• Contribute to the architecture and implementation of the computer vision stack from research to production

What we're looking for

• Master's or PhD degree in Machine learning / Computer vision
• Strong fundamentals: data structures, CV algorithms, and systems programming
• Strong C++ skills - this is critical for our edge deployment pipeline
• Solid Python proficiency for ML experimentation and tooling
• Ability to work independently, solve complex problems, and drive projects to completion
• 5+ years experience deploying computer vision models to production, ideally on resource-constrained devices
• Experience with PyTorch and model optimization for edge AI
• Proven ability to take models from research to production on embedded hardware

• Experience with NVIDIA Jetson platform, TensorRT, or Triton Inference Server
• MLOps experience (experiment tracking, model versioning, performance monitoring)
• Experience with sensor fusion (RGB, IR, depth cameras)
• Background in medical devices, regulated environments, or healthcare applications
• Experience working in fast-moving early-stage environments