Machine Learning Engineer BC

Machine Learning Engineers help deliver machine learning solutions for industrial process environments: fault detection predictive maintenance quality optimization and process control. Youll work across the full project lifecycle: scoping problems with plant engineers wrangling messy sensor data building and deploying models and making sure theyworkin 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:

• Developand 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
• Requiredomestic and international travel

Required Experience

• Degree in Engineering (Electrical Mechanical Chemical or similar) Computer Science or similar scientific/technical field

Ideal Experience

• 3-5 years of experiencein 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 (LightGBMXGBoost) deep learning frameworks (PyTorchor 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 Statesand Canadaor able to obtain appropriate work authorization (visa sponsorship may be available)
• Ability to travel domesticallyand internationallyincluding to industrial and manufacturing facilities

• Experience with process control systems (DCS/PLC) control loop tuning SCADA and MES systems
• Familiarity with OPC-UAMQTTPI Historian or similar industrial data infrastructure
• Exposure to Bayesian methods or probabilistic modeling
• Experience withMLOpstooling (MLflow Kubeflow Airflow or similar)
• Experience deploying models in edgeon-premise and cloudenvironments
• Background incontrolschemical mechanical or process engineering