Senior Manager, AV Behavior Safety Engineering

General Motors (GM)

Confirmed live yesterday High trust
Remote

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Work type
Remote
Location
Remote
Employment
Full-time
Posted
24 days ago
Freshness
Confirmed live yesterday

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Similar $202k
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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 156 open roles on FindRole.

Listed pay typically runs $159,400–$245,500 across 58 roles with salary data.

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TL;DR · Senior Manager, AV Behavior Safety Engineering

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Sr Manager, AV Behavior Safety Engineering (GPSSC) leads the strategy and execution for defining, measuring, and validating the safety of SAE Level 3–4 Automated Driving Systems powered by machine-learned models. Working within the Safety Assurance for Effective Autonomous Driving Software department, this role involves interpreting standards like ISO 21448 and ISO 5083 to develop a comprehensive safety case for autonomous vehicle behaviors. The manager will oversee the end-to-end development lifecycle, including ODD definition, simulation, and public-road validation while leading a high-performing engineering team. Key responsibilities include establishing evaluation metrics, determining sufficiency criteria, and providing technical feedback on performance across safety and reliability dimensions. Required expertise includes machine learning, data science, and robotics. Preferred technical skills include Python, PyTorch, TensorFlow, and experience with large language models, generative AI, and various cloud platforms like Azure or AWS.

Skills

ISO 21448 ISO 5083 AVSC Python R Java PySpark PyTorch TensorFlow Scikit-learn LangChain SQL LLMs Generative AI RAG Deep Learning Reinforcement Learning NLP SVM XGBoost Random Forest Decision Trees Clustering Microsoft Azure Databricks AWS Google Cloud Platform MLflow Docker Kubernetes GitHub Jira Tableau PowerBI Pandas NumPy

What you'll do

  • Interpret and apply safety standards like ISO 21448 (SOTIF) and AVSC best practices to define autonomous system development strategies.
  • Own the behavior-focused portion of the ADS Safety Case, including key claims, sufficiency criteria, and evidence for performance.
  • Define end-to-end validation methodologies for AI-driven systems in collaboration with simulation, software, and safety metrics teams.
  • Establish strategies for breaking down Operational Design Domains (ODDs) and validating performance across specific behaviors and in aggregate.
  • Develop safety performance indicators (SPIs), human benchmarks, and safety launch targets for autonomous vehicle behaviors.
  • Analyze simulation, closed-course, and public-road data to provide prioritized feedback to engineering teams on safety and reliability.
  • Manage an assurance process to verify sufficiency criteria and safety targets to support product launch readiness.
  • Lead, hire, and mentor a high-performing team of AV behavior safety engineers while growing their technical depth.

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 a related field.
  • Demonstrated experience working on production-intent AV programs and providing technical safety leadership.
  • Deep understanding of AV behavior development including ODDs, evaluation criteria, and analysis of simulation and road test data.
  • Experience applying AV safety standards such as ISO 5083, ISO 21448 (SOTIF), and AVSC practices.
  • Excellent communication skills to explain complex technical tradeoffs to executives and non-technical stakeholders.
  • 3+ years of people leadership experience, including hiring and growing high-performing technical teams.
  • 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/AI technologies 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).
  • Proficiency in data analysis and visualization tools including Tableau, PowerBI, Pandas, and NumPy (preferred).

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