Computer Vision / Deep Learning Engineer
$100k - $250k • 0.50% - 2.50%
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.
Skills: Computer Vision, Machine Learning, Python, TensorFlow, OpenCV, Deep Learning
At Overview, we don't just identify production issues. Our customers change their production processes around Overview's platform because they trust our algorithms. We are looking for someone to help us scale this culture of perfection. Someone who can work with small amounts of imperfect data and generate perfect results. Hands on responsibilities will include:
- Ownership of an entire DL project
- Identifying what kind of data and how much data to generate to get desired results
- Adding to Overview's toolbox of state of the art algorithms.
- Using small datasets to generate impressive results
- Relentlessly staying on top of the state of the art computer vision papers
- Having participated in Kaggle or other competitions
- Having taken an existing model and increased accuracy through a variety of methods
- Having worked with PyTorch and/or Tensorflow
- Implemented state of the art papers just for fun
- Having had to deploy a model with aggressive inference speed requirements
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...