Principal AV Behavior Safety Engineering Lead

General Motors (GM)

Confirmed live yesterday High trust
Remote

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Work type
Remote
Location
Remote
Posted
2 days ago
Freshness
Confirmed live yesterday

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Similar $199k
$128k most similar roles pay here $279k

This listing doesn't post a salary. Most similar roles pay $170,600–$227,500.

Based on 240 similar postings.

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About General Motors (GM)

General Motors (GM) is a leading American multinational automotive corporation founded in 1908 and headquartered in Detroit, Michigan.

General Motors (GM) currently has 116 open roles on FindRole.

Listed pay typically runs $160,200–$245,000 across 59 roles with salary data.

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At a glance

TL;DR · Principal AV Behavior Safety Engineering Lead

As a Principal AV Behavior Safety Engineering Lead within the SAFE-ADS department, you will lead the strategy and execution for defining, measuring, and validating the safety of SAE Level 3–4 Automated Driving Systems powered by machine-learned models. You will manage the behavior-focused portion of the ADS Safety Case, including sufficiency criteria and performance indicators, while collaborating with cross-functional teams to develop end-to-end validation methodologies. Your work involves interpreting standards such as ISO 21448 (SOTIF), ISO 5083, and AVSC best practices to ensure safety for next-generation architectures. The role requires expertise in machine learning, data science, and autonomous vehicle development. Preferred technical skills include Python, PyTorch, TensorFlow, and SQL, alongside experience with LLMs, Generative AI, and MLOps tools like Docker and Kubernetes to solve complex challenges in automated driving behavior and safety performance.

What you'll do

  • Interpret safety standards like ISO 21448 and AVSC best practices to define GM’s strategy for autonomous system development.
  • Own the safety strategy and technical roadmap for next-generation automated driving system architectures.
  • Manage the behavior-focused portion of the ADS Safety Case, including key claims and sufficiency criteria.
  • Define end-to-end validation methodologies for AI-driven systems in collaboration with simulation and software teams.
  • Establish strategies for breaking down Operational Design Domains (ODDs) to validate performance per specific behaviors.
  • Develop safety performance indicators (SPIs), human benchmarks, and target metrics for autonomous vehicle behaviors.
  • Analyze simulation, closed-course, and public-road data to provide prioritized feedback to engineering teams.
  • Execute an assurance process to verify safety targets and sufficiency criteria to support product launch readiness.

What we're looking for

  • Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field.
  • 8+ years of experience in machine learning, engineering, data science, or a related field.
  • 8+ years of experience in autonomous vehicle or robotics development or related fields.
  • Demonstrated experience working on production-intent AV programs.
  • Track record providing technical safety leadership in AV development including risk assessments and validation methodologies.
  • Deep understanding of AV behavior development, including defining ODDs and analyzing simulation, closed-course, and public-road data.
  • Experience applying AV safety standards such as ISO 21448 (SOTIF), ISO 5083, and AVSC practices.
  • Excellent communication skills to explain complex technical tradeoffs to executives and non-technical stakeholders.
  • Master's degree in Computer Science, Engineering, Mathematics, or a related field (preferred).
  • Proficiency in programming frameworks like Python, R, Java, PySpark, PyTorch, TensorFlow, Scikit-learn, LangChain, and SQL (preferred).
  • Experience with Machine Learning topics including LLMs, Generative AI, RAG, Deep learning, and NLP (preferred).
  • Experience with Cloud platforms such as Microsoft Azure, AWS, or Google Cloud Platform (preferred).
  • Experience with MLOps tools like MLflow, Docker, Kubernetes, GitHub, and Jira (preferred).

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