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

Bosch

Confirmed live today High trust

Quick summary

Work type: On-site
Location: Sunnyvale, CA
Salary: $240,000–$320,000 / yr
Posted: 8 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $198k

This role $280k

$141k most similar roles pay here $339k

This role pays more than 88% of similar roles. Most pay $159,737–$235,750 — the shaded band above. At the midpoint, this role pays about $280k versus about $198k for comparable roles.

Based on 240 similar postings.

Employer

About Bosch

Bosch is a global engineering and technology company supplying mobility solutions, industrial technology, consumer goods, and energy and building technology; Robert Bosch LLC is its North American arm.

Bosch currently has 34 open roles on FindRole.

Listed pay typically runs $165,000–$185,000 across 16 roles with salary data.

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At a glance

TL;DR · Senior Principal Engineer, Autonomous Driving (ADAS) Data Loop & Flywheel

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The Senior Principal Engineer- Autonomous Driving (ADAS) Data Loop & Flywheel will join the team to spearhead the architectural strategy and execution of the end-to-end continuous data engine for L2+ ADAS and autonomous driving stacks. This role involves driving the software, data loop, and MLOps machinery required to ingest raw fleet logs, curate edge cases, auto-label datasets, and retrain deep learning models for embedded automotive platforms. The position requires expertise in PyTorch, TensorFlow, Ray, Kubernetes, Triton, Spark, Python, and C++. Key responsibilities include managing active learning pipelines, model compression, and quantization for automotive-grade hardware. The work focuses on solving the technical challenge of transitioning raw fleet data into production-grade training pipelines while ensuring compliance with ISO 26262 and ISO 21448 safety standards.

Skills

MLOps PyTorch TensorFlow Python C++ Kubernetes Ray Triton Spark CI/CD SIL HIL ISO 26262 ISO 21448 SOTIF Transformers Quantization Model Compression

What you'll do

Define and execute the technical roadmap for the end-to-end autonomous driving data engine.
Oversee the architecture, development, and testing of the AI data flywheel and MLOps machinery.
Establish a rapid-evaluation framework for benchmarking and integrating emerging multimodal E2E AI solutions.
Transition raw fleet log data into scalable, production-grade training and auto-labeling pipelines.
Implement automated validation workflows and scenario-based testing aligned with automotive safety standards.
Optimize the performance of complex neural networks for deployment on automotive-grade hardware.
Mentor and lead a team of AI scientists and software engineers to ensure technical excellence.

What we're looking for

Master’s degree or Ph.D. in Computer Science, Robotics, Electrical Engineering, AI, or a 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.
Technical mastery of deep learning frameworks like PyTorch and TensorFlow, including Transformers and Occupancy Networks.
Expertise in model compression, quantization, and deployment of neural networks onto embedded automotive target platforms.
Hands-on experience architecting cloud-native distributed training infrastructures and MLOps/CI/CD platforms for petabyte-scale datasets.
Strong programming proficiency in Python and C++ (preferred).

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