Machine Learning Engineers help deliver machine learning solutions for industrial process environments: fault detection, predictive maintenance, quality optimization, and process control. You’ll work across the full project lifecycle: scoping problems with plant engineers, wrangling messy sensor data, building and deploying models, and making sure they work in production.Machine Learning Engineers DemonstrateHigh integrityA willingness to go beyond the ordinary to meet and exceed client expectationsA desire for continual challenge and development, and excellent written and verbal communication skillsReports To Operations DirectorJob Qualifications Roles and responsibilities for this job may include, but are not limited to:Develop and deploy ML models (classification, regression, anomaly detection, time-series forecasting) for industrial process applicationsCollaborate with process engineers and operators to translate domain problems into well-scoped ML tasksBuild robust data pipelines from historians, SCADA systems, and other industrial data sourcesDesign feature engineering strategies grounded in physical process understandingValidate models against real plant conditions, not just offline metricsContainerize and deploy models using Docker, with experience in Kubernetes or similar orchestration toolsSupport model monitoring, retraining workflows, and CI/CD for ML pipelinesRequire domestic and international travelRequired ExperienceDegree in Engineering (Electrical, Mechanical, Chemical, or similar), Computer Science, or similar scientific/technical fieldPay Range This position pays 120k to 180K CAD.Ideal Experience3-5 years of experience in applied ML or data science, ideally in manufacturing, process industries, or adjacent fieldsStrong Python skills: scikit-learn, pandas, NumPy as a baselineExperience with a range of ML approaches: gradient boosting (LightGBM, XGBoost), deep learning frameworks (PyTorch or TensorFlow), and unsupervised methods (clustering, autoencoders, anomaly detection)Familiarity with time-series data and the challenges that come with it (irregular sampling, sensor drift, missing data, class imbalance)Working understanding of process engineering fundamentals: heat/mass balance, process flow diagrams, and common unit operationsPractical experience with Docker; familiarity with Kubernetes, Helm, or cloud container servicesComfort working with messy, real-world data rather than clean benchmark datasetsAbility to communicate model results and limitations clearly to non-ML stakeholdersMust be eligible to work in the United States and Canada or able to obtain appropriate work authorization (visa sponsorship may be available)Ability to travel domestically and internationally, including to industrial and manufacturing facilitiesHighly Valued ExperienceExperience with process control systems (DCS/PLC), control loop tuning, SCADA, and MES systemsFamiliarity with OPC-UA, MQTT, PI Historian, or similar industrial data infrastructureExposure to Bayesian methods or probabilistic modelingExperience with MLOps tooling (MLflow, Kubeflow, Airflow, or similar)Experience deploying models in edge, on-premise, and cloud environmentsBackground in controls, chemical, mechanical, or process engineering#J-18808-Ljbffr