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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal Engineer, ML (VLA Automated Driving) - **Company:** Cariad, Inc. - **Location:** Mountain View, CA, United States - **Experience:** Expert - **Salary:** $235,520.0 - $323,044.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Computer Vision, Software Debugging, Machine Learning, Tensorflow, Software Engineering, Reinforcement Learning, Supervised Learning, Pytorch, Large Language Models, Safety Critical Systems, Deep Learning, Model Validation, Data Strategy, Information Technology, ONNX (Open Neural Network Exchange) Format, Machine Learning Operations, TensorRT, Data Selection - **Published:** September 11, 2026 - **Apply:** https://www.thejobnetwork.com/job/6ee580ca-fd6f-4975-af9e-613b14551b9d/principal-engineer-ml-vla-automated-driving ## About the Role * Deep expertise in end-to-end AI and foundation-model approaches for automated driving * Strong software engineering skills and production mindset * Excellent analytical, debugging, and technical decision-making skills * Ability to lead highly complex cross-functional efforts in ambiguous environment * Strong written and verbal communication skills * Ability to collaborate effectively across teams, geographies, and time zones Required Specialized Skills: * Deep expertise in foundation models, multimodal learning, and VLA / VLAM approaches for automated driving * Strong background in transformers, vision models, multimodal fusion, and spatio-temporal modeling * Hands-on experience with PyTorch or equivalent ML frameworks * Strong experience developing and adapting large-scale ML models for real-world systems * Experience or strong familiarity with AD/ADAS systems, including end-to-end driving models, world models, or VLA-based architectures * Strong foundation in model evaluation, error analysis, and generalization across diverse driving scenarios Desired Skills: * Experience with imitation learning, offline RL, reinforcement learning, or world-model-based training * Familiarity with quantization, pruning, distillation, and hardware-aware optimization * Familiarity with TensorRT, ONNX Runtime, or similar inference frameworks * Experience deploying models on embedded or automotive-grade hardware * Experience with simulation and large-scale evaluation pipelines for automated driving * Understanding of automotive system constraints and safety considerations for ML-based ADAS/AD systems Workplace Flexibility: * Calls, virtual meetings, and workshops overlapping with German and US business hours as needed * Occasional domestic and international travel Years of Relevant Experience: * 12+ years of experience in applied machine learning, deep learning, robotics, or automated driving * 5+ years of experience in one or more of the following: multimodal foundation models, computer vision, reinforcement learning, imitation learning, or AD/ADAS systems * Strong candidates with equivalent industry experience will be considered Required Education: * Master's degree in Computer Science, Robotics, Electrical Engineering, Applied Mathematics, or a related field Desired Education: * PhD in Computer Science, Robotics, Electrical Engineering, Applied Mathematics, or a related field ## Description The Principal Engineer, ML (VLA Automated Driving) is the technical anchor for Vision-Language-Action (VLA /VLAM) models for our Level 2++ to Level 4 Automated Driving stack. This role defines the technical direction for VLA-based driving across model architecture, training strategy, data flywheel, evaluation, and embedded deployment. The Principal Engineer bridges multimodal foundation-model advances with the realities of real-time, safety-critical automotive systems and helps turn promising research into robust in-vehicle capability. This role also serves as the technical champion for applying GenAI and agentic AI to engineering workflows, identifying and scaling high-value use cases across model development, data flywheel, evaluation, and engineering productivity. This is a highly cross-functional leadership role spanning model, data, evaluation, and deployment. Role Responsibilities: Technical Direction & Architecture * Define the technical direction for VLA / VLAM-based automated driving * Lead architecture decisions for multimodal driving models from perception and context to trajectories, actions, or driving intent * Drive technical decisions on model adaptation, planning interfaces, fallback/arbitration, latency, and generalization Model Development & Training Strategy * Design, adapt, and evolve state-of-the-art vision-language-action / multimodal foundation models for automated driving * Define training strategies across supervised learning, imitation learning, offline learning, synthetic data, and simulation-based approaches as appropriate * Drive model quality across robustness, generalization, and complex traffic interactions Data Flywheel & Platform Partnerships * Define the data strategy needed to improve model performance quickly and systematically * Partner with data and platform teams to establish a scalable flywheel across data selection, balancing, mining, labeling/annotation inputs, retraining, and evaluation * Align data and training iteration loops to measurable performance outcomes and release readiness Evaluation, Benchmarking & Deployment Readiness * Define evaluation and benchmarking strategies for route-level and scenario-level driving performance * Partner with embedded and systems teams to support deployment on target automotive hardware * Drive model evaluation, error analysis, and generalization assessment across diverse driving scenarios Technical Leadership & GenAI/Agentic Workflow * Serve as a senior technical leader across engineering and partner teams, mentoring others and helping build long-term VLA capability in Mountain View * Act as the technical champion for GenAI and agentic AI workflows within the team, identifying, validating, and helping scale high-value applications across model development, evaluation, data, and engineering productivity * Establish technical standards and best practices for scalable, production-grade ML development in safety-critical systems ## Related Videos - [Developing an AI.SDK](https://www.wearedevelopers.com/videos/198-developing-an-ai-sdk) - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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