Lead development and deployment of real-time computer vision models for edge robots: model design, optimization (quantization/pruning/TensorRT), MLOps pipelines, video and signal processing for contactless vitals, and productionizing models on embedded hardware.
The company
Norbert is building autonomous robots that deliver healthcare.
Our AI sensing platform mounts on mobile robots and does the work of a care team member—rounding on patients, capturing vitals without contact (FDA-cleared for pulse and respiratory rate, more in the pipeline), running assessments, documenting to the EMR, and escalating when something's wrong. Autonomously.
We're not building demos. We're deployed in real facilities today, monitoring hundreds of patients daily. We're solving one of healthcare's hardest problems: a global nursing shortage that will hit 40% by 2030.
We're a small, international team backed by top-tier VCs, with offices in Brooklyn and Paris. We ship things that matter.
The positionWe are looking for our lead deep learning engineer to spearhead the development of our groundbreaking sensing technology.
What you will do:- 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
- 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
- Real impact: your code provides care for patients today
- High autonomy and technical ownership - you'll shape our computer vision architecture
- Work at the intersection of cutting-edge AI, edge computing, and healthcare
- A talented, excellent, diverse and international team
- Cutting-edge stack: embedded AI, robotics, LLMs, multimodal sensing
- Talented, international team tackling meaningful problems in remote patient monitoring
- Competitive salary
- Transparent, mission-driven culture focused on continuous learning
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