Create evaluation harnesses to measure model performance
Fine-tune and retrain models using deployed fleet data
Deploy across inference surfaces including edge and cloud solutions
Build pipelines for collecting and versioning production data
Benefits
Make a real impact by improving patient care
High autonomy and ownership over AI operations in production
Opportunity to work at the nexus of AI, edge computing, and healthcare
Join a talented and diverse international team
Equity participation in company’s future
Access to cutting-edge technology stack
Be part of a transparent, mission-driven culture focused on learning
Full Job Description
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
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
Transparent, mission-driven culture focused on continuous learning
Competitive salary and equity
About Norbert Health
Norbert Health is a healthcare technology company that provides a platform for patients to connect with healthcare providers and manage their health information. The company's platform is designed to improve patient outcomes and reduce healthcare costs by providing patients with access to their medical records, appointment scheduling, and other healthcare services. Norbert Health was founded in 2018 and is headquartered in Beverly, Massachusetts.
