Head of Infrastructure, ADAS​/AD

Leading the future in luxury electric and mobility

At Lucid, we set out to introduce the most captivating, luxury electric vehicles that elevate the human experience and transcend the perceived limitations of space, performance, and intelligence. Vehicles that are intuitive, liberating, and designed for the future of mobility.

We plan to lead in this new era of luxury electric by returning to the fundamentals of great design - where every decision we make is in service of the individual and environment. Because when you are no longer bound by convention, you are free to define your own experience.

Come work alongside some of the most accomplished minds in the industry. Beyond providing competitive salaries, we're providing a community for innovators who want to make an immediate and significant impact. If you are driven to create a better, more sustainable future, then this is the right place for you.

The Head of Infrastructure, ADAS / Autonomous Driving is a senior leadership role responsible for designing, building, and operating scalable cloud and hybrid infrastructure to support ADAS and autonomous vehicle product delivery. This role owns the end‑to‑end infrastructure architecture, including cloud strategy, security models, Dev Ops practices, CI/CD and ML pipelines, and data governance for global deployments.

This is a hands‑on, builder‑focused role. The successful candidate will establish and grow an in‑house Dev Ops and infrastructure organization, define internal versus vendor ownership boundaries, and architect systems that enable reliable, secure, cost‑efficient, and compliant development of production ADAS and autonomous vehicle software. Success requires deep technical expertise, strong leadership, and experience operating in fast‑moving startup or growth environments.

You Will

* Own and define the end‑to‑end infrastructure and security architecture across cloud, on-premises, and hybrid environments.

* Design, deploy, and operate scalable cloud infrastructure and large‑scale data pipelines supporting sensor data ingestion, ML training, simulation, validation, and deployment for ADAS and autonomous vehicle systems.

* Lead efforts to modernize, optimize, and improve cost efficiency and performance of legacy infrastructure and data/ML pipelines, including identifying opportunities for architectural simplification and automation.

* Build and own CI/CD and ML pipelines supporting production AI and software workflows, with an emphasis on reliability, scalability, and efficiency.

* Implement Infrastructure‑as‑Code and automation frameworks for secure, repeatable deployments.

* Manage cloud infrastructure across AWS, OCI, Azure, or similar platforms including access control, cost governance, and resource optimization.

* Define ownership boundaries and partner effectively with IT and external vendors while retaining architectural control.

* Improve developer productivity and system reliability through tooling, observability, automation, and standardized development workflows.

* Ensure infrastructure and data platforms align with data privacy, security, and regional compliance requirements, including GDPR and region‑specific constraints for the EU, KSA, and other global markets.

* Define and track KPIs for reliability, scalability, cost efficiency, and developer experience.

* Collaborate cross‑functionally with Software, Product, Systems, Validation, Program, and Operations teams.

* Evaluate and adopt emerging infrastructure, Dev Ops, MLOps, and AI‑enabled tools where they provide clear value.

You Bring

* Hands‑on experience designing and operating cloud infrastructure architectures (AWS, OCI, Azure, or similar).

* Proven experience standing up and leading an in‑house Dev Ops or infrastructure organization.

* Strong experience with CI/CD pipelines and production ML workflows.

* Deep knowledge of Infrastructure‑as‑Code and automation tools such as Terraform and Git Ops.

* Experience operating in hybrid environments (on‑premises and cloud).

* Experience working on cost optimization, performance tuning, and modernization of large scale or legacy infrastructure and data pipelines.

* Practical understanding of data…