Student Algorithm Developer- Metro

Mobileye is a pioneer in autonomous vehicles (AV). Our team builds the core deep learning model powering many sensing tasks across Mobileye’s system — already running in production cars worldwide.We turn research into reality: inspired by recent breakthroughs, we design models optimized for the EyeQ chip that serve as the foundation for other teams. Your work will directly impact real cars and roads, shaping the safety and intelligence of millions of vehicles.We’ve built and deployed models that cut chip resource usage, improve performance, and bring multiple sensing tasks together — and we’re pushing to take them even further.If you’re passionate about deep learning and want to see your ideas move from research to production — join us to build the foundation of Mobileye’s AV technology.

What will your job look like:

• Design, develop, and implement deep learning algorithms for solving complex problems.
• Collaborate with cross-functional teams to understand requirements and develop solutions.
• Conduct research, evaluate existing models and techniques, and propose innovative approaches to improve algorithm performance and accuracy.
• Optimize existing algorithms and DNN architectures to improve performance and scalability.
• Research latest advancements in deep learning, AI architectures, multi-task learning and more, and apply them to enhance algorithm capabilities.
  
  

• M.Sc. or PhD student in Computer Science or a related discipline.
• 2+ Years of proven experience in developing deep learning algorithms in Python.
• Experience with implementing end-to-end deep learning pipelines, including data preparation, training, validation and deployment.
• Proficiency with deep learning frameworks such as TensorFlow or PyTorch.
• Excellent problem-solving skills.
  
  

• PhD student in Computer Science or a related discipline.
• Industry experience in DL/Software development.
• Experience with cloud computing platforms such as AWS.
• Proficiency with Linux environment, virtual environments, and Docker containers.
• Experience with implementation of lightweight, hardware-aware Deep Neural Networks.