Machine Learning Engineer - Canada
Greater Toronto Area, ON, Canada
Full Time
Canada
Experienced
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: Machine Learning Lead
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
This position pays 120k and 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
- 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
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