Skyrocket Ventures is a recruiting firm for hundreds of high growth technology companies that range from industry leaders to top-tier startups. This opportunity is with one of our client companies for a full-time permanent hire. Please only apply if you are authorized to work in the U.S.
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Computer Vision Research Scientist / Researcher - Augmented Reality Startup (Multiple openings from Jr. to Lead level) Location: Redwood City, CA (you can work remotely as much as you'd like, even after the pandemic)
Note: To be qualified for this position, you must have authored at least one computer vision publication (preferably several to many) in top conferences such as CVPR, ICCV, ICLR, ECCV, NIPS, or PAMI).
The company's product is in the realms of augmented reality, computer vision, deep learning, navigation, machine learning, and edge processing. The product has similarities to autonomous vehicle technology but is not for autonomous vehicles and will be available on the market in the near future.
The company has about 10 employees and 9 engineers and is about to raise series A funding.
In this position, you would be doing about 90% research and 10% coding.
The company will pay competitive salary (up to $200k or more depending on experience), plus equity which could be very lucrative.
- Authored at least one computer vision publication (preferably several to many) in top conferences such as CVPR, ICCV, ICLR, ECCV, NIPS, or PAMI).
- Expertise in both classical and deep learning computer vision techniques for object recognition, semantic segmentation, or 3D geometry.
- A Master's Degree or PhD (strongly preferred) in Computer Science or similar, with a focus on Computer Vision.
- Familiartity with OpenCV, and ability to write clean code in Python and/or C/C++.
- Experience with deep learning frameworks such as Torch, PyTorch, Keras, Caffe/Caffe2, Tensorflow, etc.
Nice to have:
- Sampling methods, optimization, graphical models, and statistical machine learning
- Experience with CUDA, OpenGL, and/or OpenCL.
- Object detection and localization.
- Object tracking.
- Semantic segmentation and image segmentation.
- SVM, random forests, ensemble models, clustering, boosting, and other non-deep learning ML methods.
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