Applied AI Engineer
At a glance
Mid-level AI Engineering role at Norbert Health. Montreal · full-time · no salary stated · we estimate CA$100,000–CA$150,000 across Canada.
Norbert Health has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Move foundation models and ML components into automated pipelines that run reliably on robots in care facilities
- Build retrieval and agent workflows for clinical reports and conversational interfaces
- Ship streaming voice systems alongside batch jobs and request-response services
- Create evaluation tests to catch regressions between models and measure results against clinical accuracy goals
- Fine-tune models with deployment data, then run them through cloud APIs, self-hosted systems and robot hardware
What you bring
- At least four years delivering ML or AI systems in production outside academia
- A computer science or engineering degree, or equivalent practical experience
- Hands-on use of PEFT or LoRA and supervised fine-tuning
- Strong Python skills and experience with ONNX, quantization and an inference runtime
- Experience with a cloud-based ML training or MLOps platform
Who this fits
This role suits a mid-level engineer comfortable owning production ML work across cloud and robot-edge deployments. You’ll need to work within regulatory constraints because some systems follow a SaMD pathway.
From the employer
The company
Norbert is building autonomous robots that deliver healthcare.
Our AI sensing platform enables existing robotic platforms to become care team members: 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, Paris, and Montreal. We ship things that matter.
The position
We're looking for an Applied AI Engineer to take our growing collection of foundation models and ML components from manually run, sometimes locally trained workflows to fully automated, production-grade MLOps pipelines: deployed reliably on robots in nursing facilities. We need someone who knows the model landscape cold, treats evaluation as a first-class engineering problem, and has strong opinions about when to prompt, RAG, fine-tune, swap, or buy.
You’ll work across cloud and edge deployments, and some of the systems you’ll touch are on a SaMD pathway, so you’ll need to be comfortable shipping under regulatory constraints.
What you’ll do
- Integrate foundation models and ML components (VLMs, LLMs, ASR/TTS, detection/segmentation, embeddings) into our production pipelines, using both open-weight models and third-party APIs
- Build RAG and agent-style orchestration for clinical reporting and conversational interfaces
- Ship real-time streaming pipelines (voice agents) alongside batch and request-response workloads
- Build evaluation harnesses that catch regressions across model swaps and measure performance against clinical-grade accuracy targets
- Fine-tune and retrain models (LoRA, PEFT, supervised fine-tuning) using data collected from our deployed fleet
- Deploy across our inference surfaces: third-party APIs, self-hosted, and on-robot edge
- Build the data flywheel: pipelines that collect, label, version, and feed production data back into model improvement
- Partner with the algorithms team (signal processing, computer vision) on integration with their lower-level pipelines
What we’re looking for
- BS in Computer Science, Engineering, or a related field, or equivalent hands-on experience
- 4+ years shipping ML/AI systems in production outside of academic settings
- Strong working knowledge of the modern foundation model landscape (open-weight LLMs and VLMs, common detection/segmentation backbones, embedding models)
- Hands-on experience with PEFT/LoRA and supervised fine-tuning
- Strong Python; comfortable with the deployment toolchain (ONNX, quantization, at least one inference runtime—TensorRT, vLLM, llama.cpp, etc.)
- Experience with a cloud ML training/MLOps platform (GCP Vertex AI, AWS SageMaker, Azure ML, or equivalent)
- Ability to work independently, solve complex problems, and drive projects to completion
Bonus points
- Edge ML deployment (Jetson, ARM, mobile NPUs)
- Real-time voice AI pipelines (STT, TTS, streaming LLM)
- Production RAG systems beyond toy implementations
- Medical devices, SaMD, or other regulated ML environments
- MLOps tooling (Weights & Biases, MLflow, DVC, etc.)
- Active learning or human-in-the-loop labeling workflows
- C++ for integrating with our computer vision pipeline
What we offer
- Real impact: your code provides care for patients today
- High autonomy and technical ownership—you’ll define how we operate AI in production
- Work at the intersection of cutting-edge AI, edge computing, and healthcare
- A talented, excellent, diverse and international team
- Equity participation in the company’s future
- Cutting-edge stack: embedded AI, robotics, LLMs, multimodal sensing
- Transparent, mission-driven culture focused on continuous learning
- Competitive salary and equity