Job Description

You will be part of a team that drives systematic and data-driven improvements to autonomous vehicle software by designing, implementing, and maintaining robust processes for evaluation and validation. We are looking for a highly motivated individual with excellent analytical skills to own end-to-end execution, improve evaluation methodologies, and communicate insights that establish confidence in the quality of our end-to-end ML stack.

In this position, you will work closely with AI/ML engineers, simulation engineers, systems engineers, and data partners to identify, analyze, monitor, and prioritize the signals used to assess performance. You will leverage simulation and on-road data to build scalable processes for evaluating coverage, metrics, uncertainty, and validation confidence.

If you are interested in having a major impact on accelerating validation confidence for ML-driven autonomy through creative problem solving, let’s chat!

• Define AV evaluation and validation processes from initial concept through implementation, including test-framework requirements, scenario and test-suite design, coverage, metrics, and the evidence needed to assess confidence in system performance.

• Design and implement scalable testing and simulation frameworks for test generation, execution, data collection, result aggregation, and reproducible analysis.

• Provide hands-on implementation of infrastructure and data solutions to assess confidence in AV performance using simulation and on-road data.

• Develop and apply methods to evaluate simulation validity, sim-to-real correlation, and the predictive value of simulation results.

• Proactively scope and identify metrics, sampling approaches, and analytical methods needed to improve evaluation workflows and close gaps in evidence.

• Contribute to automated triage and root-cause analysis strategies for AV deficiencies, regressions, and uncertainty in an end-to-end stack.

• Articulate insights, summaries, limitations, and recommendations to engineers, technical leaders, and other stakeholders based on continuous analysis of AV performance.

• Define and maintain scalable processes to identify, monitor, and improve evaluation KPIs and confidence measures.

• Help connect continuous-improvement activities to the evidence needed to support safety, systems, and downstream readiness decisions.

• Strong Python programming skills, with experience building clear, maintainable analysis, evaluation, or testing tools.

• Experience designing or implementing testing, simulation, or evaluation frameworks for complex software/hardware or cyber-physical systems.

• Experience with GitHub, Jira, or equivalent tools.

• Demonstrated end-to-end ownership, from defining a problem through delivering and communicating the desired outcome.

• A track record of analytical and systems-engineering work involving complex software/hardware systems or ambiguous AI functions.

• Experience performing root-cause analysis and applying analytical methods to system or behavioral performance data.

• Ability to creatively solve problems with limited supervision, learn quickly, and operate effectively in a fast-paced environment.

• Strong cross-functional communication skills, including the ability to communicate data-driven findings to leadership.

• Bachelor’s, master’s, or doctoral degree in engineering, physics, applied mathematics, statistics, data science, or a related discipline, or an equivalent combination of education and experience.

• Experience with autonomous vehicles, ADAS, robotics, or production-grade robotic systems.

• Hands-on experience with simulation environments, scenario generation, large-scale test execution, or analysis of simulation results.

• Experience with statistical methods for product evaluation, risk assessment, sampling, or confidence analysis.

• Experience developing data-driven metrics, scorecards, coverage measures, or regression-detection methods.

• Experience with verification and validation, simulation-to-real-world correlation, test automation, or large-scale evaluation systems.

• Passion for understanding complex robotics and AI systems and turning their behavior into measurable evidence.

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 compensation may not be representative for positions located outside of New York, Colorado, California, or Washington.
·     The salary range for this role: is $153,200 to $234,100. 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