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# Computer Vision/ML Engineer

- **Company:** Norbert Health, Inc.
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Computer Vision, C++ (Programming Language), Data Structures, Python (Programming Language), Machine Learning, Sensor Fusion, Signal Processing, System Programming, Video Editing, Pytorch, Delivery Pipeline, Deep Learning, Technical Debt, Performance Monitor, Machine Learning Operations, TensorRT, Software Version Control
- **Published:** August 9, 2026
- **Apply:** https://www.wayup.com/i-j-Computer-Vision-ML-Engineer-Norbert-Health-964017005459254/

## About the Role

+ 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
Nice to haves:

 + 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

## Description

We are looking for our lead deep learning engineer to spearhead the development of our groundbreaking sensing technology., + 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


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