Redlen Technologies is a pioneering company that has revolutionized the semiconductor crystal growth and fabrication process.
At Redlen Technologies, innovation is at the heart of everything we do. Our founder's visionary work with the Canadian Space Agency laid the foundation for our groundbreaking Cadmium Zinc Telluride (CZT) sensors, revolutionizing medical imaging, security imaging, and non-destructive testing. We are driving a quantum leap forward in medical CT scanners, elevating diagnostic accuracy while reducing radiation dose– a technological feat that has earned us global recognition. With a commitment to excellence, we have earned the trust of Fortune 500 industry customers, leading to our acquisition by Canon Inc. for $430M– a testament to our industry leadership.
With over 200 dedicated professionals, we take pride in fostering a work environment where Science, Technology, Engineering, and Math (STEM) professionals thrive. As an equal opportunity employer we demonstrate that diversity drives innovation, and we are passionate about building a future where every employee contributes to our mission-driven success. Redlen's teams, mirroring the multifaceted diversity of Canadian society, exemplify our commitment to leveraging a wide range of ideas and backgrounds as a driving force for innovation, business excellence, and pioneering advancements in STEM.
Product Performance
Redlen’s Product Performance team is dedicated to ensuring our detector modules meet the highest standards of reliability, performance, and application suitability. The team plays a critical role in connecting detector design and manufacturing to real-world imaging outcomes by driving initiatives in reliability, image quality optimization, verification, validation & test (VV&T), and data-driven performance analysis. Leveraging advanced data science techniques, robust testing protocols, and cross-functional collaboration, Product Performance ensures that every detector we deliver supports world-class photon-counting CT imaging and exceeds customer expectations.
Data Science
The Data Science team builds the models, pipelines, and tools required to convert high-throughput detector data into actionable insight. By enabling scalable analysis of reliability, production, and in-field data, they support failure mode identification, performance characterization, and system optimization. They operate as a connective layer across Product Performance, Reliability, and Image Quality, ensuring decisions are grounded in quantitative evidence.
About the Role
The Applied Machine Learning Developer develops machine learning, computer vision, and advanced analytical solutions for complex scientific, product-performance, reliability, sensor, and manufacturing data. The role works with multidisciplinary teams to translate technical problems into robust analytical solutions that improve defect detection, product characterization, process understanding, and data-driven decision-making.
Responsibilities
Machine Learning & Model Development
- Develop and validate machine learning models for classification, regression, clustering, anomaly detection, and prediction using complex scientific and manufacturing datasets.
- Design feature-engineering, model-selection, cross-validation, and evaluation strategies suitable for noisy, imbalanced, high-dimensional data.
- Evaluate when machine learning or deep learning provides meaningful value over established statistical, signal-processing, or algorithmic approaches.
Computer Vision & Image Analysis
- Develop computer-vision and image-processing algorithms for automated detection, classification, segmentation, and characterization of spatial defects and patterns.
Time-Series & Anomaly Detection
- Develop methods for time-series analysis, change detection, and anomaly detection to identify abnormal or degrading sensor behavior.
ML Workflows & Software Development
- Develop reproducible and scalable machine learning workflows for data preparation, feature engineering, model training, validation, and deployment across local and cloud-based environments, including AWS.
- Translate successful experiments and notebooks into maintainable, reusable Python and MATLAB software and analytical tools.
Model Validation & Statistical Analysis
- Quantify model uncertainty and false-positive/false-negative risk.
- Communicate model assumptions, limitations, and validation results clearly.
Cross-Functional Collaboration
- Collaborate with data scientists, software developers, data engineers, scientists, technologists, and domain experts to define problems and deliver practical solutions.
Other
- Other duties as assigned and relevant to the role.
Qualifications
Required Experience
- Approximately 5+ years developing applied machine learning solutions.
- Analysis of time-series, spatial, sensor, imaging, or other high-dimensional scientific datasets.
- Computer vision and image-processing applications using OpenCV, scikit-image, PyTorch, TensorFlow, or comparable tools.
- SQL and structured analytical datasets within complex technical environments.
Skills
- Strong proficiency in Python, including NumPy, pandas, SciPy, and scikit-learn.
- Strong understanding of supervised and unsupervised learning methods, including classification, regression, clustering, and anomaly detection.
- Solid statistical foundation, including cross-validation, precision and recall, class imbalance, uncertainty quantification, and data-leakage prevention.
- Strong software development practices, including version control, modular design, testing, code review, and technical documentation.
- Ability to translate ambiguous technical questions into well-defined analytical problems and independently develop appropriate analytical approaches.
- Familiarity with AWS services such as S3, Athena, Glue, SageMaker, Lambda, and ECR/ECS is considered an asset.
- Excellent verbal and written communication skills in English.
Personal Attributes
- Strong analytical thinking and technical judgment.
- Comfortable working with ambiguity and independently advancing complex technical problems.
- Pragmatic approach to balancing scientific rigor with business and operational priorities.
Preferred Experience
- Applied machine learning or advanced analytics supporting product performance, reliability, manufacturing, scientific, sensor, or other technically complex datasets.
- Scientific computing, algorithm development, signal processing, image processing, simulation, or experimental-data analysis using MATLAB.
- Cloud-based data processing and machine learning workflows in AWS environments.
- Modern data-lake and lakehouse architectures, including Parquet-based ecosystems.
- Deep learning techniques using PyTorch, TensorFlow, or equivalent frameworks.
- MLOps practices including Docker, MLflow, CI/CD, model deployment, and experiment tracking.
- Semiconductor, medical imaging, industrial sensing, scientific instrumentation, reliability, predictive maintenance, condition monitoring, or similar technical domains.
Education
- Master’s degree in Computer Science, Data Science, Applied Mathematics, Statistics, Physics, Electrical Engineering, Computer Engineering, or another related quantitative field.
Salary Range: $100,000 - $125,000 CAD
At Redlen, we prioritize clarity in our hiring process. The salary range posted above reflects the minimum and maximum for new hire salaries, taking into account factors such as job-related skills and expertise, relevant training and education, and business needs. It is common for ranges to span multiple levels in order to attract top talent across experience levels. It is uncommon to be hired at the top of the range; however, in exceptional cases where a candidate's skills and expertise significantly exceed the role's requirements, we may consider offering a higher salary. If you believe your skill set falls outside this range and you’re interested in exploring opportunities with Redlen, please reach out to our People & Culture Team (T.A. Specialists or Business Partners), specifying how your expertise aligns with our teams, so we can collaborate on potential opportunities. We believe in developing those that demonstrate quality performance and promise.
Total Rewards and Benefits
- Competitive salary and performance-based bonuses.
- Comprehensive health, dental, and vision insurance.
- RRSP retirement plan with employer match component.
- Professional development opportunities.
- Generous paid time off and holidays.
- Vibrant and inclusive workplace culture.
