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Applied Computer Vision / Deep Learning Engineer

Overview

Overview

Software Engineering
San Francisco, CA, USA
Posted on Wednesday, April 5, 2023

Factories are highly complex systems. In some cases the manufacturing can happen too fast for humans to interact with. In other cases, workers are exposed to dangerous, physically demanding, and repetitive tasks all day long. In either case, at Overview we help the workers by providing factories with state-of-the-art AI solutions, to act as an extra set of eyes. This reduces repetitive tasks, and helps factories increase their efficiency. We don't just identify production issues in real-time. Our customers change their production processes around Overview's platform.

We are a YCombinator backed startup based in San Francisco, California. We are a small hybrid-remote team of awesome engineers working on an end-to-end platform: camera and sensor installations in factories to AI R&D. We provide clients a simple, reliable and powerful platform allowing to monitor their factories, making everyone's job easier, thanks to advanced deep learning and computer vision.

We are looking for someone to help us scale up during our time of rapid growth, and who believes in our mission. A CV/DL engineer who wants to join our team of passionate people, striving to get the best out of our customer's data.

Responsibilities

  • Ownership of an entire CV/DL project
  • Research and improvements on our core DL models
  • 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

Helpful Experience

  • Having worked with PyTorch and/or Tensorflow
  • Having participated in Kaggle or other DL competitions
  • Having worked on CV/DL research projects
  • Having taken an existing model and increased accuracy through a variety of methods
  • Implemented state of the art papers just for fun
  • Having had to deploy a model with aggressive inference speed requirements