Principal AV Behavior Safety Engineering Lead

General Motors; Mountain View, Northern (CA, KY); Hybrid; USD 251,000 - 385,000; Full time

Benefits offered by this job

Health benefits

Company vehicle program

Tuition assistance

Retirement savings plan

Paid vacation & holidays

Job summary

General Motors is seeking a Principal AV Behavior Safety Engineering Lead to define and execute the safety strategy for SAE Level 3–4 ADS powered by machine‑learned models. You will influence launch decisions and partner with teams across Software Validation, Embodied AI, Simulation, and Safety Metrics to shape the end‑to‑end AV behavior validation approach.

You will own the ADS safety case, collaborate on evaluation metrics, and help set safety launch targets.

Qualifications

• Bachelor's degree or higher in a technical field.
• 8+ years in ML, engineering, data science, or related field.
• 8+ years in autonomous vehicle or robotics development.
• Experience on production‑intent AV programs.
• Technical safety leadership in AV development (safety strategy, risk assessments, validation).
• Understanding of AV behavior development, ODDs, evaluation criteria, and AV data analysis.
• Experience applying AV safety standards and best practices (ISO 5083, ISO 21448, AVSC).
• Excellent communication and storytelling with executives and non‑technical stakeholders.

Responsibilities

• Define and lead strategy for safe ADS development, validation, and deployment.
• Own safety strategy for next‑gen ADS architectures.
• Own ADS behavior safety case: claims, sufficiency, evidence.

Skills

Safety leadership

AV development

SOTIF ISO21448

Cross-functional influence

AI/ML systems

Education

Bachelor's degree in Computer Science, Engineering, Mathematics, or related field

Masters degree (preferred)

Tools

Python

PyTorch

TensorFlow

Scikit-learn

Docker

Kubernetes

Job description

Job Description

The Safety Assurance for Effective Autonomous Driving Software (SAFE‑ADS) department is part of GM’s Global Product Safety, System, and Certification (GPSSC) organization. Our mission is to help GM deliver trusted automated‑driving products. As the central authority for automated driving system (ADS) safety, SAFE‑ADS brings together experts from across the company to develop and maintain a comprehensive safety case including safety performance indicators for GM’s automated‑driving technologies. GM’s vision is zero crashes, zero emissions, and zero congestion—and autonomous vehicle safety is essential to achieving that vision.

Role

As Principal AV Behavior Safety Engineering Lead, you will lead the strategy and support execution for how GM defines, measures, and validates the safety of SAE Level 3 – 4 Automated Driving Systems powered by machine‑learned models. You will report into the AV Safety Strategy and Assurance organization and directly influence launch decisions for GM’s next generation of automated driving products. If you are passionate about safety, have deep experience in autonomous vehicle development and validation, and are a proven technical leader in AI/ML‑driven systems, this role is an opportunity to shape how millions of people experience safe autonomous driving.

You will:

• Lead Technically reference and interpret standards such as ISO 21448 (SOTIF), ISO 5083, and AVSC best practices to define GM’s strategy for safe autonomous system development, validation and deployment
• Own safety strategy for next generation ADS architectures
• Own the behavior‑focused portion of the ADS Safety Case, including key claims, sufficiency criteria, and recommended evidence for AV behavior safety performance
• Collaborate with Software Validation, Embodied AI, Simulation, and Safety Metrics teams to define the end‑to‑end AV behavior validation methodology for AI‑driven systems
• Set the strategy for how we systematically break down ODDs and how performance is validated per behavior and in aggregate
• Collaborate on evaluation metrics, human benchmarks, and safety launch targets for AV behaviors and overall system performance
• This includes supporting development of safety performance indicators (SPIs) for AV behaviors
• Assess AV performance across safety and reliability dimensions using simulation, closed‑course, and public‑road data and provide clear, prioritized feedback to engineering teams
• Define and run an assurance process to verify the sufficiency criteria and safety targets to support launch readiness

Your Skills & Abilities (Required Qualifications)

• 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 in autonomous vehicle or robotics development or related field
• Demonstrated experience working on production‑intent AV programs
• Track record providing technical safety leadership in AV development (e.g., defining safety strategies, risk assessments, validation methodologies, safety case contributions).
• Deep understanding of AV behavior development: defining ODDs, behaviors, and evaluation criteria; analyzing simulation, closed‑course, and public‑road test data; and generating prioritized, actionable recommendations for developers.
• Experience applying AV safety standards and best practices, such as ISO 5083, ISO 21448 (SOTIF), and AVSC practices
• Excellent communication and storytelling skills, including the ability to explain complex technical tradeoffs to executives and non‑technical stakeholders.
• Proven ability to influence cross‑functional teams (Software, AI/ML, Systems Engineering, Product, Program Management, Operations) without direct authority.
• Strong problem‑solving mindset, comfort with ambiguity, and a proactive attitude toward learning and continuous improvement.

What Will Give You A Competitive Edge (Preferred qualifications)

• Masters Degree in Computer Science, Engineering, Mathematics or related field
• Programming & Frameworks: Python, R, Java, PySpark, PyTorch, TensorFlow, Scikit‑learn, LangChain, SQL
• Machine Learning & AI: Large Language Models (LLMs), Generative AI, RAG, Deep learning, Reinforcement Learning, Natural Language Processing (NLP), SVM, XGBoost, Random Forest, Decision Trees, Clustering
• Cloud & Big Data Platforms: (Preferred Microsoft Azure (Data Lake, Machine Learning, Databricks)), Nice to Have (AWS (S3, SageMaker, Bedrock) or Google Cloud Platform (BigQuery, Dataflow, AI Platform) )
• Deployment & MLOps: MLflow, Model Monitoring & Versioning, Docker & Kubernetes, GitHub, Jira
• Data Analysis & Visualization: Tableau, PowerBI, Pandas, NumPy

Compensation

The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position, as well as geography of the selected candidate.

• The salary range for this role is $250,600 and $384,600. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position.
• Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance.
• Benefits: GM offers a variety of health and wellbeing benefit programs. Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more.
• Company vehicle: Upon successful completion of a motor vehicle report review, you will be eligible to participate in a company vehicle evaluation program, though which you will be assigned a General Motors vehicle to drive and evaluate.
• Note: program participants are required to purchase/lease a qualifying GM vehicle every four years unless one of a limited number of exceptions applies.

Benefits Overview

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This role is categorized as remote. This means the selected candidate may be based anywhere in the country of work and is not expected to report to a GM worksite unless directed by their manager.

The selected candidate will be required to travel <25% for this role.