Description
Mobileye is the world leader in automotive and driving assistance systems, developing the world's most advanced software technologies and hardware architectures.
Our group is responsible for enabling deep learning and computer vision algorithms on the Mobileye platform. The technology we develop enables state-of-the-art deep neural networks to run efficiently in vehicles – contributing to saving lives and enabling autonomous vehicles.
This is an exciting opportunity to work with highly talented engineers and be a part of a product with a lot of innovation and work on cutting-edge compiler and optimization technologies in the AI domain.
What will your job look like?
- Shape Mobileye’s product by applying cutting-edge deep learning network optimization techniques and will have the chance to influence future generations of Mobileye hardware and software.
- Develop a deep learning compiler stack that takes neural network descriptions created in frameworks such as TensorFlow and PyTorch and converts them into code suitable for execution on custom-built Mobileye hardware.
- Use state-of-the-art open-source technologies such as MLIR and work closely with the open-source community.
- Develop optimized implementations of a variety of neural network operations and new optimization techniques and algorithms to efficiently map ML models onto our state-of-the-art systems.
- Develop a toolchain that will provide a quantum leap in Deep learning capabilities and performance.
All you need is:
- BSc/MSc in Computer Science/Software Engineering from a leading university
- Knowledge of C/C++ with practical experience - significant Advantage
- Background in Deep Learning – Advantage
- Experience in LLVM/MLIR – Advantage
Mobileye changes the way we drive, from preventing accidents to semi and fully autonomous vehicles. If you are an excellent, bright, hands-on person with a passion to make a difference come to lead the revolution!
Mobileye
Artificial Intelligence
Automotive
Autonomous Vehicles
Fleet Management
Transportation
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