Top Tech Jobs & Startup Jobs

Own end-to-end autonomous-vehicle data management, annotation workflows, scenario analysis, failure investigation, data collection campaigns, and automation pipelines. Manage large-scale driving datasets, improve data quality and annotation efficiency, support mass-production validation, and coordinate data standards with global cross-functional teams.

Design, develop, and deploy deep learning algorithms and semantic understanding engines for HD mapping and automotive perception. Lead end-to-end projects from research and prototyping to training, evaluation, integration, and scalable deployment using large-scale driving datasets. Collaborate across teams to improve model accuracy, robustness, and runtime efficiency.

Lead end-to-end recruiting for a rapidly growing R&D business unit. Develop sourcing and hiring strategies, advise hiring managers, build talent pipelines, support candidates, analyze recruiting data, and lead initiatives involving candidate experience, diversity, employer branding, and EVP. Organize recruiting events and special projects while collaborating across the organization in a fast-moving environment.

Develops robust, scalable production deployment systems and inference pipelines for classical and machine learning algorithms. The role bridges machine learning research and production engineering, primarily using Python, Docker, Kubernetes, model serving frameworks, and inference optimization techniques. Responsibilities include maintaining existing deployment systems, building new production capabilities, and adopting emerging MLOps technologies for computer vision and NLP workloads.

Develop production deployment systems for classical and machine learning algorithms, building robust, scalable inference pipelines. Work primarily with Python, Kubernetes, and Docker while maintaining existing systems and implementing new MLOps, model-serving, and inference-optimization technologies. Collaborate with machine learning research and engineering teams, staying current with computer vision, NLP, distributed systems, and emerging deployment best practices.

Design and implement software to optimize deep learning models (quantization, pruning, compression) for efficient inference on low-power automotive hardware. Develop in Python and PyTorch, collaborate with algorithm teams, run literature research and POCs, and support full software lifecycle including testing and CI/CD.

Lead technical planning, execution, and delivery of ADAS production programs for automotive customers. Responsibilities include coordinating stakeholders, communicating technical requirements, organizing workshops and testing, removing delivery obstacles, facilitating decisions, defining program metrics, resolving conflicts, and improving processes. The role requires customer-facing technical project management experience, strong communication and organizational skills, an engineering or computer science degree, and fluency in English and Hebrew.

Leads Mobileye’s Release Management team and oversees end-to-end software releases for ADAS and autonomous vehicle products. Owns release planning, schedules, dependencies, risk mitigation, governance, readiness criteria, stakeholder communication, metrics, documentation, and continuous process improvement. Manages Release Managers and partners with engineering, QA, product, integration, and program teams. Drives automation, CI/CD, configuration management, and DevOps best practices across complex software delivery environments.

Own the technical interface between Mobileye and automotive OEM customers. Lead system understanding, issue analysis, solution definition, software integration, validation, and delivery for ADAS and autonomous-driving solutions. Translate customer feedback into technical requirements, coordinate cross-functional investigations, and support workshops, test drives, tuning sessions, and on-site activities. Build trusted customer relationships while improving technical workflows and supporting product development.

Design, implement, and maintain computer vision and 3D reconstruction algorithms in Python. Move algorithms from research to production, collaborate across teams, and develop solutions for multi-sensor perception and real-world autonomous driving challenges.