Medical Imaging AI Engineer (Hybrid)

Socket.dev; Toronto; Hybrid; CAD 110,000 - 130,000; Full time

Benefits offered by this job

Hybrid work

On-site gym

On-site daycare

Parking

Stocked kitchen

Job summary

Clarius Mobile Health is seeking a Machine Learning Engineer to contribute to a two-year project expanding access to ultrasound technology. You will develop, train, and deploy ML models in both research and production settings, striving for scalable, robust solutions and production-ready pipelines.

Expect collaboration with researchers and engineers, a focus on performance and safety, and potential extension beyond the initial term.

Qualifications

• Bachelor's or Master's degree in Computer Science, Machine Learning, or equivalent field.
• 3+ years of industry experience in machine learning roles.
• Strong background in deep learning and computer vision.
• Proficiency in Python development on Unix/Linux and clean code practices.
• Experience with TensorFlow and PyTorch.
• Familiarity with software engineering basics: version control, testing, and code quality.

Responsibilities

• Develop, train, and deploy ML models for research and production environments.
• Improve and maintain ML tools for robustness and scalability.
• Design and optimize ML data pipelines for efficient processing and training.
• Automate ML tasks including retraining, evaluation, and monitoring in production.
• Collaborate with researchers and software engineers to translate ML research into applications.

Skills

Deep learning

Computer vision

Python development

Unix/Linux

TensorFlow

PyTorch

Version control

Testing

Communication

Education

CS/ML degree

Tools

Docker

Kubernetes

AWS

GCP

Job description

Clarius Mobile Health is seeking a Machine Learning Engineer to contribute to a two-year project expanding access to ultrasound technology. You will develop, train, and deploy ML models in both research and production settings, striving for scalable, robust solutions and production-ready pipelines.

Expect collaboration with researchers and engineers, a focus on performance and safety, and potential extension beyond the initial term.