Senior AI Systems Engineer- Autonomous Driving

Bosch Group•Sunnyvale, CA

3d•$185,000 - $215,000

About The Position

Bosch is seeking a Senior AI Systems Engineer to set technical direction across multiple teams and build and enhance AI-focused systems engineering and testing frameworks. The role guides the design, implementation, and evaluation of Advanced Driver Assistance Systems (ADAS) and Autonomous Driving technologies, serving as technical lead for a cross-functional team. It applies modern software development practices to support safe, reliable autonomous vehicles and global R&D teams. The engineer will develop intelligent failure-triaging and automated root-cause-analysis capabilities for large-scale simulation and real-world vehicle logs, integrate these capabilities into testing and release pipelines, and establish engineering practices for log data, analytics, retrieval, and ML pipeline reliability. The position partners with autonomy software, validation, and cloud infrastructure teams to integrate automated diagnostics into R&D workflows.

Requirements

Master’s degree or Ph.D. in Computer Science, Computer Engineering, Robotics, Electrical Engineering, or a related technical field, or equivalent practical experience.
At least 5 years of progressive engineering experience in software development, data analytics infrastructure, or automated testing for autonomous vehicles, robotics, or complex distributed systems.
At least 2 years of experience leading technical architecture, setting engineering strategy, or guiding technical direction across multiple engineering teams.
Expert proficiency in Python and C++.
Demonstrated experience building production-grade data pipelines, distributed execution frameworks, or testing infrastructure.
Hands-on experience with big data structures, robotics logging formats such as ROS/ROS2, MCAP, Protobuf, or CAN logs, and cloud data storage.

Nice To Haves

Experience applying LLMs/VLMs, sequence modeling, pattern recognition, or vector embeddings to log classification, failure-mode retrieval, or automated root-cause analysis.
In-depth understanding of autonomous-driving software architectures, including perception, sensor fusion, localization, motion planning, and vehicle control.
Experience operating SiL, HiL, or cloud-based replay and simulation testing setups at scale.
Experience with distributed frameworks and data tools such as PySpark, Ray, Databricks/Snowflake, vector databases, Apache Airflow, Kubernetes, and AWS/GCP services.

Responsibilities

Architect and scale intelligent failure-triaging and automated root-cause-analysis frameworks for petabyte-scale simulation and real-world vehicle logs.
Apply AI/ML methods, including LLMs/VLMs, time-series anomaly detection, statistical pattern recognition, and causal inference, to isolate failure modes across perception, planning, control, and vehicle platform layers.
Integrate automated triaging into continuous integration, regression testing, and SiL/HiL/ViL cloud replay pipelines.
Establish best practices for log data structures, automated analytics workflows, vector search and embeddings for failure-mode retrieval, and ML pipeline reliability.
Partner with autonomy software leads, validation engineers, and cloud infrastructure teams to align toolsets and integrate automated diagnostics into R&D workflows.
Set technical direction across teams and guide the design, implementation, and evaluation of ADAS and autonomous-driving technologies.