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 learning systems.
Exceptional technical leadership, mentoring skills, and cross-functional communication abilities.

Employment type
Full-time

Work arrangement
Hybrid