Applied AI Engineer

About the role

Norbert‑Health is building autonomous robots that deliver healthcare. The Applied AI Engineer will transform foundation models and ML components into fully automated, production‑grade MLOps pipelines that run reliably on robots in nursing facilities, while navigating regulatory constraints.

Key responsibilities

• Integrate foundation models and ML components (VLMs, LLMs, ASR/TTS, detection/segmentation, embeddings) into production pipelines, using open‑weight models and third‑party APIs.
• Build retrieval‑augmented generation (RAG) and agent‑style orchestration for clinical reporting and conversational interfaces.
• Ship real‑time streaming pipelines (voice agents) alongside batch and request‑response workloads.
• Develop evaluation harnesses to catch regressions across model swaps and meet clinical‑grade accuracy targets.
• Fine‑tune and retrain models (LoRA, PEFT, supervised fine‑tuning) using data collected from the deployed fleet.
• Deploy models across inference surfaces: third‑party APIs, self‑hosted services, and on‑robot edge devices.
• Build data‑flywheel pipelines that collect, label, version, and feed production data back into model improvement.
• Partner with the algorithms team on integration with lower‑level signal‑processing and computer‑vision pipelines.

Required profile

• BS in Computer Science, Engineering or related field, or equivalent hands‑on experience.
• 4+ years of experience shipping ML/AI systems in production environments.
• Deep knowledge of modern foundation model landscape (open‑weight LLMs, VLMs, detection/segmentation backbones, embedding models).
• Hands‑on experience with PEFT/LoRA and supervised fine‑tuning.
• Strong Python skills and familiarity with deployment toolchains (ONNX, quantization, TensorRT, vLLM, llama.cpp, etc.).
• Experience with cloud ML training/MLOps platforms such as GCP Vertex AI, AWS SageMaker or Azure ML.
• Ability to work independently, solve complex problems and drive projects to completion.

Required skills

• Python
• ONNX
• Quantization
• TensorRT
• vLLM
• llama.cpp
• GCP Vertex AI
• AWS SageMaker
• Azure ML
• PEFT
• LoRA
• LLMs
• VLMs
• ASR/TTS
• Detection and segmentation models
• Embedding models
• MLOps tooling (Weights & Biases, MLflow, DVC)
• Edge deployment platforms (Jetson, ARM, mobile NPUs)
• C++ (optional for computer‑vision integration)

What we offer

• Real impact: code that provides care for patients today.
• High autonomy and technical ownership of AI production systems.
• Work at the intersection of cutting‑edge AI, edge computing, and healthcare.
• Diverse, international team with equity participation.
• Competitive salary and equity.