VP Engineering for Development Infrastructure Group

we are seeking a highly technical and experienced Vice President of Engineering to lead the Development Infrastructure Group, a team of over 180 engineers, project/product managers, and support engineers. This role is responsible for the architecture, development, and operational excellence of our companys core infrastructure that powers big data processing, high-performance cloud computing, and large-scale re-simulation for autonomous vehicle development. The ideal candidate will have a strong technical background in software engineering, distributed systems, cloud computing, and data engineering, along with a proven ability to manage large teams and budgets in a fast-paced environment.
What your job will look like;
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 our companys 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.