Applied AI Engineer: Edge ML for Healthcare Robots

Norbert Health; Paris; Hybrid; EUR 90.000 - 130.000; Vollzeit

Benefits dieser Stelle

Equity participation in the company’s/

Zusammenfassung

Norbert Health is hiring an Applied AI Engineer to scale our foundation models into production-grade MLOps pipelines deployed on robots in nursing facilities. You will work across cloud and edge deployments, ensuring regulatory compliance for SaMD pathways and real-time clinical capabilities.

The role demands strong Python skills, a deep understanding of modern foundation models, and hands-on experience with PEFT/LoRA and supervised fine-tuning.

Qualifikationen

• 4+ years shipping ML/AI systems in production outside academia.
• Strong knowledge of foundation models (open-weight LLMs and VLMs).
• Hands-on with PEFT/LoRA and supervised fine-tuning.

Aufgaben

• Integrate foundation models and ML components into production pipelines using open-weight models and third-party APIs.
• Build RAG and agent-style orchestration for clinical reporting and conversational interfaces.
• Ship real-time streaming pipelines alongside batch/workloads.
• Create evaluation harnesses to catch regressions across model swaps and measure clinical-grade accuracy.
• Fine-tune and retrain models (LoRA, PEFT, supervised fine-tuning) from deployed data.
• Deploy across inference surfaces: third-party APIs, self-hosted, and on-robot edge.
• Collaborate with algorithms team on integration with lower-level pipelines.

Kenntnisse

Production ML

Foundation models

PEFT/LoRA

Python

MLOps platforms

Ausbildung

BS in Computer Science or Engineering

Tools

ONNX

TensorRT

vLLM

llama.cpp

MLflow

Jobbeschreibung

Norbert Health is hiring an Applied AI Engineer to scale our foundation models into production-grade MLOps pipelines deployed on robots in nursing facilities. You will work across cloud and edge deployments, ensuring regulatory compliance for SaMD pathways and real-time clinical capabilities.

The role demands strong Python skills, a deep understanding of modern foundation models, and hands-on experience with PEFT/LoRA and supervised fine-tuning.