About
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 position
We 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
What we look 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
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
What we offer:
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 and equity
Transparent, mission-driven culture focused on continuous learning
Languages
- English
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