Machine Learning Engineer

The Human-Machine Interaction Lab unites global talents to redefine the relationship between humans and technology. Focused on innovation and user-centered design, the lab strives to advance human-computer interaction research. Our team includes researchers, engineers, and designers collaborating across disciplines to develop novel interactive systems, sensing technologies, wearable and IoT systems, human factors, computer vision, and multimodal interfaces. Through high-impact products and cutting-edge research, we aim to enhance user experiences and interactions with technology.

• Develop and deploy machine learning models for: Solar and wind power forecasting, supporting both real-time operations and longer-term energy planning

• Build data-driven components for renewable plant digital twin platforms, including: Real-time generation simulation and monitoring; Weather-driven scenario analysis; Performance diagnostics and anomaly detection

• Work with diverse data sources, such as: SCADA and historical generation signals; Meteorological measurements and NWP forecasts; Satellite imagery and sky-imager video streams; Plant layout, spatial topology, and turbine/panel metadata

• Collaborate with cross-functional engineering and research teams to deliver practical, production-ready solutions.

The total target annual compensation for this position ranges from $106,000 to $156,000 depending on education, experience, and demonstrated expertise.

• Master’s or PhD degree in: Machine Learning / Computer Science; Applied Mathematics / Statistics; Meteorology / Atmospheric Science; Energy Systems / Electrical Engineering

• Strong foundation in machine learning, deep learning, and statistical modeling

• Proficiency in Python and modern deep learning frameworks (PyTorch is an asset)

• Experience with computer vision and image/video processing, such as: Remote sensing imagery understanding; Spatio-temporal video modeling; Vision Transformers or related architectures

• Familiarity with physics-informed learning, including: Incorporating physical constraints into neural models; Hybrid modeling combining data-driven and domain knowledge

• Ability to work with real-world industrial sensor data and build robust forecasting pipelines.

Huawei Canada is committed to a fair, inclusive, and accessible recruitment process. If you require accommodation during any stage of the hiring process, please let us know and we will work with you to meet your needs.

All applications for this position are reviewed directly by our hiring team, we do not use artificial intelligence tools to screen or select candidates.