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 Demonstrate High integrity A willingness to go beyond the ordinary to meet and exceed client expectations A desire for continual challenge and development, and excellent written and verbal communication skills Reports To Operations Director Job 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 applications Collaborate with process engineers and operators to translate domain problems into well-scoped ML tasks Build robust data pipelines from historians, SCADA systems, and other industrial data sources Design feature engineering strategies grounded in physical process understanding Validate models against real plant conditions, not just offline metrics Containerize and deploy models using Docker, with experience in Kubernetes or similar orchestration tools Support model monitoring, retraining workflows, and CI/CD for ML pipelines Require domestic and international travel Required Experience Degree in Engineering (Electrical, Mechanical, Chemical, or similar), Computer Science, or similar scientific/technical field Pay Range This position pays 120k to 180K CAD. Ideal Experience 3-5 years of experience in applied ML or data science, ideally in manufacturing, process industries, or adjacent fields Strong Python skills: scikit-learn, pandas, NumPy as a baseline Experience 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 operations Practical experience with Docker; familiarity with Kubernetes, Helm, or cloud container services Comfort working with messy, real-world data rather than clean benchmark datasets Ability to communicate model results and limitations clearly to non-ML stakeholders Must 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 facilities Highly Valued Experience Experience with process control systems (DCS/PLC), control loop tuning, SCADA, and MES systems Familiarity with OPC-UA, MQTT, PI Historian, or similar industrial data infrastructure Exposure to Bayesian methods or probabilistic modeling Experience with MLOps tooling (MLflow, Kubeflow, Airflow, or similar) Experience deploying models in edge, on-premise, and cloud environments Background in controls, chemical, mechanical, or process engineering #J-18808-Ljbffr