Sr Director, Autonomous Driving & Machine Learning

We are CARIAD, an automotive software development team with the Volkswagen Group. Our mission is to make the automotive experience safer, more sustainable, more comfortable, more digital, and more fun. To achieve that we are building the leading tech stack for the automotive industry and creating a unified software platform for over 10 million new vehicles per year. We’re looking for talented, digital minds like you to help us create code that moves the world. Together with you, we’ll build outstanding digital experiences and products for all Volkswagen Group brands that will transform mobility. Join us as we shape the future of the car and everyone around it.

​Role Summary:

The Senior Director of Engineering – Autonomous Driving & Machine Learning serves as the technical, organizational, and strategic leader for the US-based End-to-End Autonomous Driving (E2E ADAS) domain. This role owns the definition, architecture, execution, and delivery of a one- stage, machine-learning-driven ADAS solution spanning data, model development, cloud infrastructure, embedded deployment, and vehicle integration.

The role balances long-term technical vision, people leadership, and deep engineering judgment, remaining actively engaged in critical architectural decisions, system performance optimization, safety, and deployment readiness. The Senior Director is accountable for engineering outcomes, organizational health, and cross-company alignment across CARIAD Inc., CARIAD SE, Volkswagen Group Innovation, and key technology partners.

Role Responsibilities:

Engineering Strategy & Architecture

• Define and evolve the long-term technical roadmap for E2E autonomous driving systems
• Set architectural standards, engineering principles, and quality bars across teams
• Own key technical tradeoffs and serve as final escalation point for complex system decisions

Organizational & People Leadership

• Build, lead, and scale a high-performing engineering organization
• Develop senior managers and technical leaders; drive succession and capability growth
• Own hiring strategy, organizational design, onboarding, and talent development

Execution & Delivery Accountability

• Ensure predictable delivery of complex, cross-functional programs
• Own engineering outcomes related to system performance, reliability, safety, and readiness
• Maintain accountability for integration, validation, demo readiness, and on-road testing

Cross-Functional & Global Alignment

• Partner closely with Product, Safety, Validation, and Business leadership
• Coordinate with CARIAD SE and global stakeholders on strategy, risks, and execution
• Represent the E2E ADAS domain in senior technical and planning forums

Technical Oversight & Governance

• Provide executive-level review of critical design decisions and risk areas
• Establish governance for technical reviews, metrics, documentation, and continuous improvement

​General Skills:

• Executive-level engineering leadership in machine learning for autonomous driving
• Ability to define long-term technical strategy and translate it into execution
• Deep understanding of end-to-end ML system lifecycles (data, training, evaluation, deployment, monitoring)
• Strong judgment in technical tradeoffs, risk management, and prioritization under real-world constraints
• Excellence in scaling teams, processes, and platforms for sustained delivery

​Required Specialized Skills:

• Ownership of end-to-end AV stack architecture across perception, prediction, planning, and control
• Deep expertise in ML/DL architectures for AV (CNNs, transformers, multi-modal fusion, foundation or policy models)
• Experience operating cloud-based ML platforms for large-scale training and data management
• Strong background in embedded deployment and real-time performance optimization
• Knowledge of functional safety, SafeAI principles, and regulatory considerations for autonomous systems

​Desired Skills:

• Experience with simulation-first development and closed-loop validation
• Familiarity with large-scale dataset governance, labeling strategies, and compliance
• Practical experience with imitation learning (IL) and reinforcement learning (RL) for driving policies

​Workplace Flexibility:

•  Primarily on-site at US development location to enable close collaboration with engineering