Senior AI Systems Engineer- Autonomous Driving

Bosch Group•Sunnyvale, CA

About The Position

Bosch is seeking a Senior AI Systems Engineer to set technical direction across teams and strengthen AI-focused systems engineering and testing frameworks for Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD). The role leads the design, implementation, and evaluation of these technologies, serving as technical lead for a cross-functional team and helping global R&D teams develop safe, reliable autonomous vehicles. The engineer will build scalable systems for intelligent failure triaging and automated root cause analysis (RCA) across petabyte-scale simulation and real-world vehicle logs. The work applies modern AI/ML approaches to identify failure modes across perception, planning, control, and vehicle platform layers, and integrates automated diagnostics with continuous integration, regression testing, and cloud replay pipelines. The position partners with autonomy software leads, validation engineers, and cloud infrastructure teams to establish data and analytics practices, align toolsets, and integrate automated diagnostics into everyday R&D workflows. The California salary range is $185,000–$215,000, with an annual corporate bonus and a long-term incentive bonus.

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, and 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 with 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 DBs, 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 cloud replay pipelines, including SiL, HiL, and ViL.
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.
Guide the design, implementation, and evaluation of ADAS and autonomous driving technologies.
Set technical direction across multiple teams and influence architecture, engineering standards, and technical capabilities.