Senior Software Systems Engineer, Autonomous Systems Validation Confidence

General Motors is seeking a Senior Software Systems Engineer to develop methods, software, and quantitative evidence for measuring confidence in autonomous vehicle validation results. The role combines software engineering, systems engineering, simulation, statistics, and data science to evaluate testing coverage, simulation validity, metrics, regressions, and release readiness.

Responsibilities:

• Develop scalable frameworks and methods for measuring validation confidence across simulation and real-world testing, including coverage, sampling, metric quality, statistical significance, and regression detection
• Build tools and data pipelines for test execution, analysis, metric computation, scorecards, and confidence reporting
• Evaluate simulation validity and road predictive power using measurable, defensible criteria; analyze test and vehicle data to identify uncertainty, pipeline issues, regressions, and gaps in evidence
• Translate validation claims and release questions into requirements, experiments, test suites, metrics, and quantitative decision criteria
• Improve the throughput, repeatability, and quality of validation workflows
• Partner with simulation, safety, autonomy, and release teams, and communicate conclusions, assumptions, limitations, and recommendations clearly

Requirements:

• Strong programming skills in Python, C++, or a comparable language, with experience writing clear, testable, maintainable code
• Experience applying engineering or quantitative methods to a physical, cyber-physical, or other real-world system
• Ability to turn ambiguous validation questions into measurable requirements, metrics, experiments, or decision criteria, and investigate complex behavior using incomplete or noisy data
• Ability to collaborate across disciplines and explain technical results with appropriate precision and context
• Bachelor's degree in engineering, physics, applied mathematics, statistics, data science, or a related technical field, or equivalent practical experience
• Experience with autonomous vehicles, robotics, simulation, aerospace, industrial automation, or another safety-relevant engineered system
• Experience with verification and validation, test automation, scenario generation, requirements-based testing, or performance benchmarking
• Experience designing coverage measures, scorecards, confidence metrics, or regression-detection methods
• Experience with simulation-to-real-world correlation, predictive-validity analysis, or comparing results across test environments
• Applied knowledge of probability, statistics, experimental design, or data analysis, including hypothesis testing, confidence intervals, power analysis, sampling strategies, or precision and recall
• Experience with data pipelines, SQL, scientific computing, or large-scale test execution
• Graduate degree or equivalent depth in engineering, physics, applied mathematics, statistics, data science, or a related field