Software Engineer, Computer Vision and Deep Learning
$0 - $0 • 0.00% - 0.00%
Mashgin builds self-checkout kiosks that use A.I. to scan multiple items without barcodes, reducing checkout time by 10x. We’re completely recreating the checkout experience in an industry that’s seen little innovation in decades. We are live in several locations across the country and processing 200k transactions monthly. We are growing fast. This tweet captures it best (https://twitter.com/ilyasu/status/1039333344452857856). (See also, Tigers vs. Mashgin: https://twitter.com/tigers/status/1034478219750191106)
We were part of YC W15 and have raised $11M+ from investors like Matrix Partners, KPCB, Venrock and more.
Skills: Deep Learning, Computer Vision
- Developing new computer vision algorithms in C/C++ and Python for solving challenging real world problems.
- Coming up with large scale data collection techniques for training Deep Neural Nets.
- Driving the development of new algorithms that dramatically improve our existing methods.
- Researching and maintaining state-of-the-art ML/CV algorithms that can analyze images.
YOU MUST HAVE
- Masters degree in Computer Science or related field.
- Strong background in Computer Vision.
- Exposure to new Deep Learning techniques for image recognition.
- MS/PhD degree or equivalent practical experience in Computer Science, AI, Machine Learning, or related technical field.
- Experience with Python or C/C++ in a Linux environment.
- Knowledge and experience in application of Deep Learning to Computer Vision problems.
- Real-world experience building Computer Vision systems.
WHAT WE OFFER
- An opportunity to work on a small, multidisciplinary team with the potential to break new ground in many different industries.
- Location: a few minute walk from the San Antonio Caltrain station in Mountain View, across the street from the San Antonio shopping center.
- Food: free meals and snacks while at the office.
- Excellent health and dental insurance.
- Flexible PTO policy.
- Competitive salary and options in a small, rapidly scaling company.
Our system reconstructs the entire scene in 3D in real-time using multiple cameras and uses deep learning to separate, identify and count items.
3D Computer Vision, Convolutional Neural Nets, C++, Python, Node.js.