COMPANY INTRODUCTION

Foqus is developing a software solution that integrates with existing MRI scanners to reduce lengthy MRI scans (approximately 60 minutes) to less than 5 minutes, without compromising quality. The software utilizes proprietary AI-based reconstruction algorithms to produce high-quality MRI images from incomplete raw data.
Foqus’ first AI-powered software has been tested on more than 20,000 MRI images and validated by MRI radiologists with positive feedback. The software was successfully used in a pilot study and has received FDA 510(k) clearance and the Health Canada Medical Device License, ready to be launched in the U.S. and Canada.
Backed by VC investments, we have built a strong team and established consolidated product design and development processes. In addition, we have begun generating revenue through some initial collaborations and plan to achieve profitability in the coming years.

JOB DESCRIPTION

We are looking for a strong candidate with machine learning experience in medical imaging (or image processing) to join our team as an ML Engineer to help with the development, deployment and maintenance of our machine learning modules. These machine learning modules are used for reconstruction of MRI scans and are trained on terabyte-scale raw MRI datasets. This role will work closely with our engineering team to support building and deploying our machine learning pipeline.

BASIC QUALIFICATIONS

• University degree in an engineering or technical field such as Computer Science or Computer Engineering.
• Familiarity or experience with modern architectures and training techniques, such as foundation models, generative AI, self-supervision, contrastive learning, etc.
• Proficiency in Python, both for library/package development and scripting.
• Solid knowledge and experience in machine learning and deep learning concepts, both in theory and application.
• Proficiency in PyTorch.
• Linux development experience.
• Understanding of version control best practices.
• Experience with data preprocessing.
• Experience writing and maintaining unit and integration tests.

NICE-TO-HAVE QUALIFICATIONS

• Knowledge of MRI physics or experience with MRI data/techniques.
• Knowledge of CI/CD techniques and technologies.
• Experience with handling medical data, especially MRI scans and MRI raw data.
• Experience in High-Performance Computing, Parallel Programming, and cloud infrastructure (e.g., for setting up training tasks).
• Experience working in a regulated industry (medical device industry preferred).

ADDITIONAL INFORMATION

We are based in Toronto and while being in Toronto can be helpful for this position, this is a remote position.
Foqus is committed to a diverse and inclusive workplace. Foqus is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, disability, age, or other legally protected statuses. Candidates from under-represented groups are strongly encouraged to apply.
This position involves a lot of learning and growth opportunities. So if you are a curious person and are interested in expanding your knowledge, experience, and skills; if you like working with a team of intelligent people in a vibrant, agile, and safe environment; and if you are looking for a chance to make a difference, this is your chance, apply for this position.
Job Types: Full-time, Permanent, Internship / Co-op
Pay: $50,000.00-$200,000.00 per year

Benefits:

• Flexible schedule

Work Location: Remot

• Curation and preprocessing of large and complex MRI scan data.
• Design, implementation, and optimization of ML models using mainstream AI/ML frameworks, with emphasis on medical image data.
• Development of efficient pipelines for training and testing models.
• Research and prototype new algorithms and paradigms, such as foundation models, self-supervised learning, generative models, etc.
• Integration of trained models with Foqus’ software solution.
• Design, implement, and evaluate data synthesis and augmentation strategies, including generative techniques.
• Hyperparameter tuning of the models and pipelines.
• Designing experiments for investigating and identifying parameters that influence model performance.
• Monitor the performance of deployed models and iterate on improvements
• Support the development of demos and implementation of pilots
• Investigate technical problems, ensure design quality, provide recommendations and lead solutions
• Documentation including writing reports, papers, or patents
• Help with filling out documents for our quality management systems (e.g., development procedures, validation reports, etc.)