You will be part of Mobileye's REM (Road Experience Management) department which is responsible for the automatic High Definition map-making process, which is a key technology in Mobileye's autonomous driving and high-end Driving Assistance systems.
You will be joining the team that builds the state-of-the-art AI-powered relevancy systems at scale. You will be designing and implementing semantic understanding engines that power data-driven products across autonomous driving and beyond, combining cutting-edge Computer Vision, Graph Neural Networks, and efficient inference to solve real-world perception challenges.
Conduct hands-on research and development of AI and Deep Learning algorithms for real-world automotive perception tasks.
Own projects end-to-end โ from concept, through research, prototyping, designing, training, and evaluating neural network architectures, to integration and scalable deployment.
Analyze challenging edge cases and continuously improve model performance in complex driving scenarios.
Collaborate with algorithm developers, software engineers, product managers and system teams across the organization.
Work with large-scale datasets and real-world driving scenarios to improve model accuracy, robustness, and runtime efficiency.
Bring your ideas into production-ready systems that have a direct, measurable impact on autonomous vehicle technologies used worldwide.
B.Sc. or M.Sc. in Computer Science, Electrical Engineering, or a related field, with strong academic performance.
3+ years of hands-on experience developing deep learning solutions using Python and frameworks such as PyTorch.
Solid foundation in Algorithms, Machine Learning, and Deep Learning fundamentals (Computer Vision / Graph Neural Networks โ advantage).
Strong analytical skills, a sense of ownership, and the ability to work collaboratively.
Design, build, and optimize AI infrastructure for Ambient.ai's real-time intelligence platform, focusing on deep learning models and multimodal AI systems.
As an Algorithm Developer on the REM Change Detection team, you will design and implement deep learning engines to process crowd-sourced data for real-time road and infrastructure updates.
Forward Deployed Engineers (FDEs) lead complex end-to-end deployments of frontier models in production, collaborating closely with strategic customers to drive adoption and impact.
Forward Deployed Engineers (FDEs) lead complex end-to-end deployments of frontier models in production, collaborating closely with customer teams to ensure successful adoption and impact.
Forward Deployed Engineers (FDEs) lead complex end-to-end deployments of frontier models in production, partnering with customers to ensure successful adoption and impact.
