Job Summary

We are seeking a highly motivated AI / ML / LLM Engineer to join our core technology team in Canada. The ideal candidate will have hands-on experience with training, fine-tuning, and deploying machine learning and large language models (LLMs) in real-world healthcare and embedded applications. This role involves working with multimodal data (text, image, time-series), optimizing models for edge deployment (e.g., on Jetson platforms), and collaborating with clinical and engineering teams to develop intelligent decision-support tools.

Key Responsibilities:

Model Development & Training: Design and implement ML models and LLMs for clinical NLP, computer vision, and time-series analysis.

LLM Fine-Tuning & Optimization: Fine-tune transformer-based LLMs (e.g., LLaMA, GPT, BERT) using domain-specific data.

Multimodal Integration: Fuse data from EMR notes, physiological monitors, and video sources to build robust predictive systems.

Edge AI Inference: Optimize and deploy ML/LLM models on edge platforms such as NVIDIA Jetson (Xavier, AGX Orin).

Pipeline Automation: Build scalable training and inference pipelines using PyTorch, TensorFlow, and Hugging Face Transformers.

Evaluation & Validation: Define performance metrics, conduct clinical validations, and refine models for safety-critical environments.

Collaboration & Documentation: Work with firmware, frontend, and clinical teams; maintain technical documentation and research records.

Required Qualifications:

3+ years of experience in AI/ML development, including work on NLP and LLMs

Proficiency in Python, PyTorch, and TensorFlow

Hands-on experience with Hugging Face Transformers, LangChain, or OpenLLM frameworks

Familiarity with NVIDIA CUDA, TensorRT, and ONNX for edge model acceleration

Strong understanding of neural networks, model interpretability, and training workflows

Experience with Git, version control, and collaborative development practices

Solid grasp of medical or clinical datasets (notes, time-series, imaging) preferred

Preferred Qualifications:

Experience deploying models on Jetson platforms (Nano, TX2, Xavier, Orin)

Understanding of HIPAA, PHI, and secure model deployment in healthcare

Knowledge of classical ML algorithms (XGBoost, SVM, clustering) for tabular data

Experience with multimodal model architectures (e.g., vision-language models)

Publications or participation in medical AI challenges (e.g., PhysioNet, MIMIC)

Why Join Us?

Develop impactful AI solutions for neonatal and critical care

Work at the intersection of healthcare, deep learning, and edge computing

Collaborate with top clinicians, engineers, and researchers

Competitive salary, benefits, and the opportunity to shape next-gen AI products