Overview is a company that takes the cutting edge in computer vision and deep learning and applies it to previously unsolvable manufacturing inspection problems. We are truly a full stack company. We install physical cameras into the facility, run inference on the edge and manage massive deployments. Overview also streams gigabytes of video/image data to the cloud for our web platform to give customers advanced insights and analytics.
We are looking for engineers who like to solve tough challenges across the stack. This might include wrangling 500mb images or deploying optimized models in one click that can handle 10 parts a second. We are looking for people who are dynamic, who are excited to work on a different challenge every week, whether it's the final inspection on a medical device or making sure a razor blade has a perfect edge.
Company Overview
Overview.ai is a leading innovator in next-generation computer vision technology for complex manufacturing. Our smart camera is revolutionizing manufacturing efficiency and quality, backed by top-tier investors. This is an opportunity to join a team that’s not only impacting the industrial vision technology sector but also shaping global manufacturing standards.
Job Description
We are seeking a detail-oriented and experienced Full Stack Engineer to join our team. The ideal candidate will be responsible for designing and building production software for an embedded AI camera product.
At Overview, you would be working on problems such as:
1. Solving customer problems that span the true full stack, from the frontend to the backend
to flipping bits in low-level hardware.
2. Leveraging new web technologies to run transformer models in the browser.
3. Deploying and optimizing the state-of-the-art AI at the edge for mission-critical
applications on the NVIDIA Jetson.
If working on these problems excite you, we would love to talk to you!
Responsibilities
Qualifications
Preferred Qualifications
Overview's tech is far reaching. We deploy highly reliable camera hardware and GPU's into manufacturing and industrial settings. We have an IoT fleet management stack to update, secure, and manage our fleet of devices. We stream this data to our cloud or to a customers hybrid cloud where we have a user facing platform where customers can interact with their devices, deploy new deep learning models, and see advanced analytics.
We use Docker to deploy web infrastructure and deep learning models. We have an extensive deep learning stack using Tensorflow, PyTorch and packaged with Onnx. Our frontend is in React, which is updated in real time from our fleet using MQTT. And the list goes on...
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