{"id":3711288,"url":"https://alion.io/job/redlen-applied-machine-learning-developer","title":"Applied Machine Learning Developer","company":{"id":2153592,"name":"Redlen","domain":"redlen.com","url":"https://alion.io/company/redlen-com","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Dayforce","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Saanichton, Canada"],"countries":["CA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon ECS","optional":false},{"name":"Amazon S3","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"Computer Vision","optional":false},{"name":"Machine Learning","optional":false},{"name":"MATLAB","optional":false},{"name":"OpenCV","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"scikit-image","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"Time Series","optional":false},{"name":"CI/CD","optional":true},{"name":"Docker","optional":true},{"name":"MLFlow","optional":true},{"name":"NumPy","optional":true},{"name":"SciPy","optional":true}],"status":"live","first_seen_at":"2026-11-03T08:00:00Z","employer_posted_date":"2026-11-03","last_verified_at":"2026-11-03T23:21:35Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Product Performance\nRedlen’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.\nData Science\nThe 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.\n\nAbout the Role\nThe 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.\nResponsibilities\nMachine Learning & Model Development\nDevelop and validate machine learning models for classification, regression, clustering, anomaly detection, and prediction using complex scientific and manufacturing datasets.\nDesign feature-engineering, model-selection, cross-validation, and evaluation strategies suitable for noisy, imbalanced, high-dimensional data.\nEvaluate when machine learning or deep learning provides meaningful value over established statistical, signal-processing, or algorithmic approaches.\nComputer Vision & Image Analysis\nDevelop computer-vision and image-processing algorithms for automated detection, classification, segmentation, and characterization of spatial defects and patterns.\nTime-Series & Anomaly Detection\nDevelop methods for time-series analysis, change detection, and anomaly detection to identify abnormal or degrading sensor behavior.\nML Workflows & Software Development\nDevelop reproducible and scalable machine learning workflows for data preparation, feature engineering, model training, validation, and deployment across local and cloud-based environments, including AWS.\nTranslate successful experiments and notebooks into maintainable, reusable Python and MATLAB software and analytical tools.\nModel Validation & Statistical Analysis\nQuantify model uncertainty and false-positive/false-negative risk.\nCommunicate model assumptions, limitations, and validation results clearly.\nCross-Functional Collaboration\nCollaborate with data scientists, software developers, data engineers, scientists, technologists, and domain experts to define problems and deliver practical solutions.\nOther\nOther duties as assigned and relevant to the role.\n\nQualifications\nRequired Experience\nApproximately 5+ years developing applied machine learning solutions.\nAnalysis of time-series, spatial, sensor, imaging, or other high-dimensional scientific datasets.\nComputer vision and image-processing applications using OpenCV, scikit-image, PyTorch, TensorFlow, or comparable tools.\nSQL and structured analytical datasets within complex technical environments.\nSkills\nStrong proficiency in Python, including NumPy, pandas, SciPy, and scikit-learn.\nStrong understanding of supervised and unsupervised learning methods, including classification, regression, clustering, and anomaly detection.\nSolid statistical foundation, including cross-validation, precision and recall, class imbalance, uncertainty quantification, and data-leakage prevention.\nStrong software development practices, including version control, modular design, testing, code review, and technical documentation.\nAbility to translate ambiguous technical questions into well-defined analytical problems and independently develop appropriate analytical approaches.\nFamiliarity with AWS services such as S3, Athena, Glue, SageMaker, Lambda, and ECR/ECS is considered an asset.\nExcellent verbal and written communication skills in English.\nPersonal Attributes\nStrong analytical thinking and technical judgment.\nComfortable working with ambiguity and independently advancing complex technical problems.\nPragmatic approach to balancing scientific rigor with business and operational priorities.\nPreferred Experience\nApplied machine learning or advanced analytics supporting product performance, reliability, manufacturing, scientific, sensor, or other technically complex datasets.\nScientific computing, algorithm development, signal processing, image processing, simulation, or experimental-data analysis using MATLAB.\nCloud-based data processing and machine learning workflows in AWS environments.\nModern data-lake and lakehouse architectures, including Parquet-based ecosystems.\nDeep learning techniques using PyTorch, TensorFlow, or equivalent frameworks.\nMLOps practices including Docker, MLflow, CI/CD, model deployment, and experiment tracking.\nSemiconductor, medical imaging, industrial sensing, scientific instrumentation, reliability, predictive maintenance, condition monitoring, or similar technical domains.\nEducation\nMaster’s degree in Computer Science, Data Science, Applied Mathematics, Statistics, Physics, Electrical Engineering, Computer Engineering, or another related quantitative field.\n\nSalary Range: $100,000 - 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