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…
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See And Understand The World
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.
Currency and observation date were not supplied; this is not a current valuation or a funding round.
Invision AI has 8 open jobs in Canada as of November 6, 2026. Top locations: Toronto.
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Invision AI
Invision AI needs a Master's or PhD in mechanical, electrical or computer engineering with a mechatronics focus. Candidates also need CAD experience, sensor and embedded integration skills, industry experience in transportation, robotics, automotive or embedded systems, and field demo experience.
SolidWorks, Python, LiDAR, Industrial computers
Invision AI
5+ YOECandidates need 5+ years of software, embedded, or field engineering experience, strong C++ and Linux skills, solid networking knowledge, and hands-on hardware troubleshooting. Customer communication and willingness to do field work are also required.
Linux, C++, Go, Elixir, Docker, MQTT
Invision AI
5+ YOECandidates need 5 years of experience deploying web applications, with Elixir and Phoenix LiveView, another framework such as Django or Ruby on Rails, and relational databases. Strong experience with Docker, Linux, Kubernetes, AWS or Google Cloud, and modern engineering practices is also required.
Elixir, Phoenix LiveView, Docker, Kubernetes, Linux, Nginx
Invision AI
Candidates need a track record of shipping production computer vision models and strong Python skills, with experience in PyTorch, TensorFlow or scikit-learn. They also need experience optimizing models for edge devices, sound software engineering practices, and the legal right to work in Canada.
PyTorch, TensorFlow, scikit-learn, Docker, ONNX Runtime, TensorRT
Invision AI
Candidates need a master's or PhD in mechanical, electrical or computer engineering with a mechatronics focus, plus hands-on experience with CAD (SolidWorks preferred), sensor selection and embedded systems. Strong project leadership, field installation experience and Python skills are also required.
SolidWorks, LiDAR, Python, PyTorch, TensorFlow, AWS
Invision AI
Candidates need a track record of shipping production computer vision models, strong Python skills, and experience with PyTorch, TensorFlow or scikit-learn. Working knowledge of embedded systems and edge optimization is required, and candidates must be legally entitled to work in Canada.
PyTorch, TensorFlow, scikit-learn, Docker, ONNX Runtime, TensorRT
Invision AI
5+ YOECandidates need 5+ years deploying web applications, Elixir and Phoenix LiveView experience, and another framework such as Django or Rails with relational databases. Kubernetes, AWS or Google Cloud, Docker, Linux, and strong communication skills are also required.
Elixir, Phoenix LiveView, Docker, Kubernetes, Linux, Nginx
Invision AI
5+ YOECandidates need 5+ years of software, systems, IoT, embedded or field engineering experience, strong C++ and Linux skills, solid networking knowledge, and hands-on hardware and software troubleshooting. Customer communication and willingness to travel for field work are also required.
C++, Linux, Docker, Go, Elixir, MQTT
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