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

Bosch Group•Sunnyvale, CA

About The Position

The Senior Principal Engineer – ADAS & AV Data Loop & AI Flywheel will lead the architectural strategy, design, and execution of the end-to-end continuous data engine supporting Bosch XC’s L2+ ADAS and autonomous driving stacks across entry-, mid-, and high-tier vehicle platforms. The systems include driving, parking, and interior sensing applications. The role is the chief technical authority for the software, data-loop, and MLOps machinery that ingests fleet logs, curates edge cases, auto-labels datasets, retrains deep learning models, and validates releases for embedded automotive platforms. The engineer will set the technical roadmap for fleet data-loop automation, active learning, auto-labeling, simulation, and MLOps tooling; oversee architecture, development, and testing; and integrate edge fleet triggers, cloud data lakes, model repositories, and automotive target hardware. The role also works with data engineering, cloud infrastructure, and embedded runtime SOC teams to scale the integrated AI ecosystem. The position establishes rapid evaluation frameworks for benchmarking, active-learning selection, and continuous integration of emerging multimodal AI approaches. It guides research prototypes and fleet logs into production-grade training and auto-labeling pipelines, optimizes runtime performance on automotive hardware, and implements automated validation and scenario-based SIL/HIL testing aligned with automotive safety standards. The role also mentors and leads AI scientists and software engineers.

Requirements

Master’s degree or Ph.D. in Computer Science, Robotics, Electrical Engineering, AI, or a closely related field focused on autonomous systems.
10 or more 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 PyTorch or TensorFlow and foundational AI paradigms including Transformers, Occupancy Networks, and Reinforcement or Imitation Learning.
Expertise in model compression, quantization, and deploying complex neural networks on embedded automotive target platforms (SOCs).
Hands-on experience architecting cloud-native distributed-training infrastructure, high-throughput data-processing pipelines, and MLOps/CI/CD platforms for petabyte-scale fleet datasets, such as Ray, Kubernetes, Triton, or Spark.

Nice To Haves

Hands-on experience developing offline, high-precision auto-labeling frameworks using 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 and ISO 21448/SOTIF) applied to deep learning systems.
Exceptional technical leadership, mentoring skills, and cross-functional communication abilities.

Responsibilities

Define and execute the technical roadmap and strategy for the end-to-end autonomous-driving data engine, including fleet data-loop automation, active-learning pipelines, auto-labeling, simulation, and MLOps tooling.
Oversee the architecture, development, and testing of the AI data flywheel and its integration with edge fleet triggers, cloud data lakes, model repositories, and automotive target hardware.
Collaborate with data engineering, cloud infrastructure, and embedded runtime SOC teams to define, drive, and scale the integrated AI machinery ecosystem.
Establish rapid-evaluation frameworks for benchmarking, active-learning selection, and continuous integration of emerging multimodal end-to-end AI solutions.
Guide raw fleet-log data and research prototypes into scalable, production-grade training and auto-labeling pipelines, optimizing runtime performance on automotive-grade hardware.
Use experience launching AI-based L2+ systems to implement automated validation workflows, scenario-based SIL/HIL testing, and continuous feedback loops aligned with automotive safety standards.
Mentor and lead AI scientists and software engineers, establishing technical excellence in automated data engines and large-scale AI machinery.