This role is open to being 100% remote within Canada while our home office is located in Toronto, Ontario. You may be required to travel to Toronto once per quarter for team and/or company events.  

Who You’ll Be Joining:

You will be part of the dynamic data engineering and machine learning services group at ecobee focused on leveraging data to enhance the smart home experience for customers. This team is responsible for building and maintaining the data infrastructure and machine learning capabilities that power intelligent features across ecobee’s product ecosystem, such as integrated AI services, energy optimization, home automation, personalized climate control, predictive maintenance.

How You’ll Make an Impact:   

• Design Scalable Product Architecture: Develop robust, scalable architectures that support machine learning capabilities within the Energy and Smart Security business at ecobee, integrating multiple data sources (thermostat telemetry, geofence signals, motion events, etc.) while ensuring designs fit seamlessly with ecobee’s broader architecture and meet long-term scalability needs.
• Complex Problem Solving: Tackle high-complexity problems that require detailed cross-domain knowledge, addressing significant ambiguity, and working with incomplete data. Collaborate across teams to solve challenges where solutions impact multiple domains.
• Architectural Impact: Contribute to ecobee’s system architecture with designs that have been battle-tested, resulting in significant, long-lasting impact within a specific domain. Solutions are expected to integrate elegantly with ecobee’s broader enterprise architecture and align with company-wide standards.
• Enterprise-Wide Architecture: Start to think beyond individual components or domains, considering ecobee’s broader architectural strategy. Collaborate with principal engineers and directors to ensure designs complement the company’s vision.
• Ownership & Delivery: Take end-to-end ownership of full components within your domain of expertise, ensuring that their design, implementation, testing, deployment, and operations meet high standards. These components will likely interact with systems in other domains, requiring careful consideration of cross-team dependencies.
• Code Quality & Debugging: Consistently deliver high-quality, maintainable code. Lead by example in debugging tough, stack-wide issues, finding root causes, and implementing effective solutions.
• Forward-Thinking & Strategic Initiatives: Anticipate future challenges and propose technical solutions that prevent future roadblocks. Participate in evaluating and recommending new technologies and frameworks for the product group.
• Mentorship & Collaboration: Actively mentor other engineers, guiding them toward concrete goals and fostering a culture of feedback and knowledge sharing. Lead or participate in design reviews, post-mortems, and code reviews.
• Innovation & Impact: Drive innovative improvements to processes and best practices across the engineering organization. Contribute to novel solutions that influence ecobee’s engineering direction and deliver measurable, high-impact results across multiple teams and domains.
• Cross-team Collaboration: Facilitate and lead discussions across squads, ensuring inclusive decision-making processes and cross-functional buy-in on technical solutions and business decisions. Serve as a trusted advisor and leader within ecobee’s engineering community.

What You’ll Bring to the Table:    

• 10+ years of experience in software engineering, with a proven track record of owning and delivering complex, cross-domain projects at scale.
• Expertise in system design, architecture, and the development of large-scale, high-availability and security systems.
• Very high proficiency in multiple programming languages and frameworks including Python, Java, Go, Node.js, etc.
• Expertise in messaging queue processing (e.g., RabbitMQ, Kafka) and real-time data handling.
• Experience with device telemetry, understanding its limits, and how to design systems that effectively use telemetry data to enhance functionality.
• Experience working with with deep learning architectures and frameworks (e.g. Pytorch, Tensorflow) and leveraging such frameworks to build scalable features
• Familiarity with MLOps stacks like Kubeflow, MLFlow, Sagemaker and proven experience in deploying machine learning features at scale
• Experience working with cloud platforms such as AWS, Azure, or Google Cloud.
• Experience optimizing database performance and system tuning, ensuring that database and application interactions are fast, reliable, and scalable.
• Demonstrated ability to debug tough, stack-wide issues across multiple environments, finding root causes and implementing long-term fixes.
• Demonstrated ability to mentor, lead technical discussions, and contribute to a collaborative engineering culture.
• Experience with DevOps principles, CI/CD pipelines, and ensuring operational excellence.
• Familiarity with IoT technologies and connected devices is an asset.
• Strong debugging skills, with experience solving complex, stack-wide issues that involve mobile devices and cloud-based services.

Just so you know: The hired candidate will be required to complete a background check. 

What happens after you apply:   

Application review. It will happen. By an actual person in Talent Acquisition. We get upwards of 100+ applications for some roles, it can take a few days, but every applicant can expect a note regarding their application status.  

Interview Process:  

• A 30-minute phone call with a member of Talent Acquisition  
• 45 minutes interview with the candidate with Director of Engineering. This is to discuss experience designing and building scalable data architectures, pipelines, and processing systems.
• 90 minutes with staff and senior engineers for technical interview on System Design & Architecture and coding challenge
• The final interview will be a 90 minutes interview divided into two parts where you will meet with the Director and VP of Engineering