Provide technical vision and strategic direction for all three infrastructure domains.
Participate in design and architecture discussions, ensuring technical trade-offs align with business goals.
Conduct code reviews, mentor engineers, and ensure best practices in software development.
Interface with top management, internal stakeholders, and external customers, advocating for infrastructure improvements and driving adoption of advanced technologies.
Identify and implement best practices in CI/CD, observability, security, and automation.
Architect and manage high-performance computing (HPC) infrastructure for large-scale machine learning training and validation workloads.
Lead the development of internal cloud services and SaaS solutions that enable Mobileye’s customers to access essential tools and applications.
Ensure efficient use of Kubernetes, Argo Workflows, Terraform, and cloud-based GPU computing (AWS, GCP, Azure).
Oversee the development of real-time simulation platforms for autonomous vehicle testing and enable the simulation of thousands of hours of drives in offline environments.
Guide engineering efforts in low-level, high-performance software development, data center software optimization, and parallel computing.
Oversee the development of end-to-end data pipelines from ingestion to access by internal and external customers.
Define and optimize data models for cataloging and data lakes.
Drive the use of Apache Spark, Hadoop, Kafka, Parquet, and Delta Lake for efficient data processing and storage.
All you need is:
15+ years of experience in software engineering, cloud infrastructure, or big data systems.
10+ years of leadership experience, managing large, distributed engineering teams.
Expertise in distributed computing, cloud-native architectures, high-performance computing, and large-scale data processing.
Hands-on experience with Kubernetes, Apache Spark, Argo Workflows, Terraform, AWS/GCP/Azure, Kafka, and CI/CD pipelines.
Strong proficiency in Python, C++, and other relevant programming languages.
Deep understanding of ML training pipelines, data engineering, and simulation technologies.
Proven track record of leading cross-functional teams, mentoring managers, and fostering innovation.
Strong ability to interface with executives, engineers, and customers to align technical execution with business objectives.
Experience with budget planning, vendor management, and cost optimization strategies.