AI-driven infrastructure for modern property management - ON-1172

Project type: Research
Desired discipline(s): Engineering - computer / electrical, Engineering, Computer science, Mathematical Sciences
Company: CMAI
Project Length: 6 months to 1 year
Preferred start date: 03/02/2026
Language requirement: English
Location(s): Toronto, ON, Canada
No. of positions: 2
Desired education level: Master'sPhDPostdoctoral fellow
Open to applicants registered at an institution outside of Canada: Yes

About the company: 

CMAI is an advanced Artificual Intelligence (AI) technology company focused on transforming property management operations through artificial intelligence driven operation management and workflow automation.  The firm not only unifies the entire end-to-end property managment workflows, but also leverages properties’ data, maintaince records and regulations to build a predictive serivce model to significantly drive property management efficiency, energy and reserouce saving, environment and social sustainbility, driving asset value growth.

Describe the project.: 

The property management industry—spanning commercial, rental, and residential sectors - is one of the most delayed sectors embraced advanced AI technology and digital transformation, making a significantly change of the business operation model and process. While vast amounts of operational data are generated daily, most organizations lack the right data infrastructure and suitable AI-driven tools to exert data value effectively, boosting productivity and driving asset value growth. The primary goal of this research project is to bridge this gap by developing a proprietary, end-to-end data ecosystem designed to enhance operational efficiency and strategic decision-making.

This project aims to research and develop two interdependent technical deliverables. The first is a comprehensive data collection and governance framework. This involves establishing rigorous protocols for data collection, validation, and privacy compliance. By moving away from fragmented, third-party dependencies, the company aims to build and maintain a secure, high-quality "in-house" dataset. This sovereign data foundation is critical for ensuring compliance with modern privacy standards while acting as the scalable backbone for future application development.

The second deliverable is a fully functional predictive modeling engine. Utilizing this curated dataset, the project will develop and train advanced machine learning algorithms. These models will move beyond simple descriptive analytics (what happened) to predictive analytics (what will happen), offering capabilities such as forecasting maintenance needs, predicting property maintenance, and optimizing asset utilization.

Ultimately, the goal is not merely to create software, but to establish a validated methodology for integrating AI into property management. By successfully build a robust governance structure with high-level machine learning, this project will deliver a competitive advantage, transforming raw building data into actionable, high-value business intelligence.

Required expertise/skills: 

• Core Programming: Proficiency in Python (pandas, NumPy, scikit-learn and etc) or R.
• Machine Learning: Experience with predictive modeling, regression analysis, and time-series forecasting.
• Data Engineering: Ability to clean, merge, and manage large, messy datasets. Experience with SQL and NoSQL databases, Vector database.
• Asset: Knowledge of Canadian privacy standards (PIPEDA) or experience handling sensitive PII data.