Massive amounts of data being created at the edge, uploading this data to the cloud for real-time analysis is neither financially nor technically feasible. Extracting meaning from this data is computationally intensive and expensive, limiting adoption of edge AI to a few high-value applications. After years of research by leaders in computer vision and machine learning we have developed technology that is two to three orders of magnitude more efficient than other high-accuracy deep learning systems. Our patent pending technology evolves the state of the art with a combination of optimization, sprinkled with unique insight and just a little magic. Video is our first application: the system is trained in the cloud by ingesting video where objects and activities of interest have been annotated. Learning is distilled into ultra-efficient software that runs on fixed, body-mounted and vehicle-mounted cameras. We can enable analytics on most professional IP video cameras with just a firmware update. Older cameras can be retrofitted with a low-power embedded processor, such as those from our partners NXP and Ambarella. Our software-only system gets smarter over time: edge cases (where the system is not very confident that it knows what is happening) are captured, uploaded to the cloud for analysis, and the learning distilled into ever smarter software. We work with more than just video: infrared, thermal, radar and lidar play important complimentary roles in helping to establish accurate, detailed situational awareness across a wide variety of environmental conditions. Each sensor detects, classifies and tracks multiple objects locally in real-time. This metadata is then push to the cloud, or a central ECU in the case of a vehicle, where it is fused to establish ground truth, detect behavior, track objects across multiple sensors, and identify anomalies.
Hybrid · Toronto
You will develop, evaluate, and deploy computer vision models across the full machine learning lifecycle for real-world products. Additionally, you will optimize these models for edge devices while maintaining production pipelines and monitoring performance.
Hybrid · Toronto
Develop, deploy, and maintain production computer vision models across the full machine learning lifecycle. Optimize models for resource-constrained edge devices and establish evaluation methods to connect performance to business outcomes.
On-site · Toronto
Own the complete product lifecycle of the Vehicle Occupancy Detection system, bridging high-level strategy with hardware selection and edge software deployment. Lead field execution, including roadside commissioning and technical communication with end-customers.
Hybrid · Toronto
You will take ownership of the design and development of cloud and edge infrastructure, web applications, and APIs. Additionally, you will prototype new features and implement client-side and server-side architectures to support various product lines.
Hybrid · Toronto
You will own the end-to-end product lifecycle of the flagship Vehicle Occupancy Detection system, bridging high-level strategy with hardware selection and edge software deployment. Additionally, you will lead cross-functional engineering teams and serve as the primary technical bridge for end-customers during field execution and commissioning.
On-site · Toronto
Design and develop cloud and edge infrastructure, web applications, and APIs for a universal AI platform. Implement both client-side and server-side architectures while prototyping innovative features using cutting-edge technologies.
