Senior Principal Engineer- Autonomous Driving (ADAS) Data Loop & Flywheel

As the Senior Principal Engineer – ADAS & AV Data Loop & AI Flywheel, you will spearhead the architectural strategy, design, and execution of the end-to-end continuous data engine powering Bosch XC’s L2+ ADAS and autonomous driving stacks (e.g., driving, parking, interior sensing) across entry, mid, and high-tier vehicle platforms. You will serve as the chief technical authority driving the software, data loop, and MLOps machinery that automatically ingests raw fleet logs, curates high-value edge cases, auto-labels datasets, retrains deep learning models, and validates releases for embedded automotive platforms. Key Responsibilities • Define and execute the technical roadmap and strategy for the E2E Autonomous Driving Data Engine, including fleet data loop automation, active learning pipelines, auto-labeling, simulation, and MLOps tooling. • Oversee the end-to-end architecture, development, and testing of the AI data flywheel and its seamless interaction with edge fleet triggers, cloud data lakes, model repositories, and automotive target hardware. • Collaborate closely with cross-functional leads (data engineering, cloud infrastructure, embedded runtime SOC teams) to define, drive, and scale the integrated AI machinery ecosystem. • Establish a rapid-evaluation development framework that accelerates the benchmarking, active learning selection, and continuous integration of emerging multimodal E2E AI solutions (e.g., Transformers, Occupancy Networks, Vision-Language models). • Guide the transition of raw fleet log data and research prototypes into scalable, production-grade training and auto-labeling pipelines, ensuring runtime performance optimization on automotive-grade hardware. • Leverage prior industry experience launching AI-based L2+ systems to implement automated validation workflows, scenario-based testing (SIL/HIL), and continuous feedback loops aligned with automotive safety standards (ISO 26262, ISO 21448 / SOTIF). • Mentor and lead a high-caliber team of AI scientists and software engineers, establishing technical excellence in automated data engines and large-scale AI machinery. Basic Qualifications: • Master’s degree or Ph.D. in Computer Science, Robotics, Electrical Engineering, AI, or a closely related field focused on autonomous systems. • 10+ years of software development and system architecture experience in ADAS or Autonomous Driving applications. • Proven industry track record of taking AI-based L2+ or L3/L4 autonomous driving systems into mass production. • Deep knowledge of End-to-End AI architecture, model training algorithms, and data flywheel concepts (including active learning, fleet edge-triggers, and automated data curation). • Deep technical mastery of modern deep learning frameworks (PyTorch, TensorFlow) and foundational AI paradigms (Transformers, Occupancy Networks, Reinforcement/Imitation Learning). • Expertise in model compression, quantization, and deployment of complex neural networks onto embedded automotive target platforms (SOCs). • Hands-on experience architecting cloud-native distributed training infrastructures, high-throughput data processing pipelines, and MLOps / CI/CD platforms for petabyte-scale fleet datasets (e.g., Ray, Kubernetes, Triton, Spark). Preferred Qualifications: • Hands-on experience developing offline high-precision auto-labeling frameworks (utilizing multimodal foundation models, 3D perception fusion, or generative AI engines). • Experience integrating closed-loop simulation engines (SIL/HIL) and synthetic scenario generation into AI retraining pipelines. • Strong programming proficiency in Python and C++. • Deep understanding of functional safety and safety-of-the-intended-functionality standards (ISO 26262, ISO 21448 / SOTIF) applied to deep learn