AI/ML Engineer

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We are excited to partner with AI-Spectral Technology Corp. in their search for a Optical Engineer.

LOCATION: On-site Kanata Ottawa Ontario Canada

DEPARTMENT: AI department

JOB TYPE: Full-time

COMPANY

AI-Spectral Technology Corp. is a leading innovator specializing in advanced ophthalmic medical instruments. We focus on retinal diagnostic imaging and treatment solutions leveraging Multi-Spectral Imaging (MSI) technology combined with AI. Since 2005 weve been at the forefront of developing innovative medical imaging systems. Based in Kanata Ontario we are a privately held company with a global team of professionals driving advancements in healthcare.

POSITION OVERVIEW

As an AI/ML engineer you will play a crucial role in developing and deploying state-of-the-art deep learning and machine learning models for retinal image analysis. Your contributions will enhance diagnostic precision optimize clinical workflows and facilitate the early identification of ocular diseases. You will also contribute actively to our AI research and innovation pipeline prototyping novel algorithms and working closely with clinicians and researchers to translate cutting-edge ideas into impactful healthcare solutions.

KEY RESPONSIBILITIES

• Design implement and evaluate scalable and novel deep learning (or machine learning) solutions using cloud-native platforms (such as Azure) and distributed computing techniques.
• Integrate anatomical priors into AI-driven workflows ensuring high-quality preprocessing annotation integration and augmentation strategies.
• Develop and maintain advanced ML Ops pipelines with testing retraining and observability baked in.
• Translate dynamic business goals and compliance standards into actionable AI platform features and modular production-ready ML frameworks.
• Uphold the technical strategy for model explainability data privacy regulatory compliance and responsible AI governance.

• Contribute to model deployment pathways in a way that bridges research and production.

• Keep up with state-of-the-art research in ophthalmology AI and translate findings into production-ready applications.

• Help prepare visualizations experiments and evaluation reports for internal review and research publication.

• Collaborate with domain experts to annotate datasets define relevant biomarkers and refine evaluation criteria.

• Collaborate with radiologists ophthalmologists data scientists and software engineers to build clinically viable AI solutions.

• Participate in regulatory-compliant model development and documentation for FDA submission (if applicable).

JOB REQUIREMENTS

Minimum Qualifications:

• Bachelors or Masters degree in Computer Science Biomedical Engineering Electrical Engineering Computer Engineering or a related field.
• 2 years of experience in developing deep learning models for medical image analysis.
• Strong foundation in machine learning deep learning and data structures/algorithms (especially AI for healthcare).
• Strong programming skills in Python and familiarity with deep learning libraries (e.g. PyTorch TensorFlow).
• Experience working with medical image formats.
• Basic knowledge of medical image pre-processing.
• Curiosity eagerness to learn and attention to reproducibility and detail.
• Excellent interpersonal teamwork written and verbal communication skills.
• Fluency in English.

Preferred Skills:

• Master/PhD or bachelors with multiple years of practical experience

• Experience with cloud infrastructure (AWS/Azure) for model training and deployment.
• Prior experience deploying models in production environments.
• Knowledge of retinal imaging radiology or pathology-specific challenges.
• Contributions to open-source ML projects or published academic work in medical imaging.
• Git/GitHub experience and good coding/documentation habits.
• Knowledge of model explainability uncertainty quantification or causal inference in medical AI.

SCHEDULE: This is an immediate opening at our Kanata Ontario office.

CONTACT: To apply or inquire about this position please send your resume a cover letter and any links to relevant publications (Google Scholar) or project portfolios (GitHub) to