설명
The Role
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!
What you’ll be doing
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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.
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Design and implement scalable testing and simulation frameworks for test generation, execution, data collection, result aggregation, and reproducible analysis.
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Provide hands-on implementation of infrastructure and data solutions to assess confidence in AV performance using simulation and on-road data.
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Develop and apply methods to evaluate simulation validity, sim-to-real correlation, and the predictive value of simulation results.
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Proactively scope and identify metrics, sampling approaches, and analytical methods needed to improve evaluation workflows and close gaps in evidence.
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Contribute to automated triage and root-cause analysis strategies for AV deficiencies, regressions, and uncertainty in an end-to-end stack.
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Articulate insights, summaries, limitations, and recommendations to engineers, technical leaders, and other stakeholders based on continuous analysis of AV performance.
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Define and maintain scalable processes to identify, monitor, and improve evaluation KPIs and confidence measures.
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Help connect continuous-improvement activities to the evidence needed to support safety, systems, and downstream readiness decisions.
What you must have
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Strong Python programming skills, with experience building clear, maintainable analysis, evaluation, or testing tools.
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Experience designing or implementing testing, simulation, or evaluation frameworks for complex software/hardware or cyber-physical systems.
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Experience with GitHub, Jira, or equivalent tools.
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Demonstrated end-to-end ownership, from defining a problem through delivering and communicating the desired outcome.
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A track record of analytical and systems-engineering work involving complex software/hardware systems or ambiguous AI functions.
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Experience performing root-cause analysis and applying analytical methods to system or behavioral performance data.
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Ability to creatively solve problems with limited supervision, learn quickly, and operate effectively in a fast-paced environment.
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Strong cross-functional communication skills, including the ability to communicate data-driven findings to leadership.
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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.
Bonus points!
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Experience with autonomous vehicles, ADAS, robotics, or production-grade robotic systems.
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Hands-on experience with simulation environments, scenario generation, large-scale test execution, or analysis of simulation results.
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Experience with statistical methods for product evaluation, risk assessment, sampling, or confidence analysis.
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Experience developing data-driven metrics, scorecards, coverage measures, or regression-detection methods.
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Experience with verification and validation, simulation-to-real-world correlation, test automation, or large-scale evaluation systems.
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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
다양성 정보
General Motors는 법적으로 금지된 차별을 배제하는 것은 물론 포용성과 소속감을 진정으로 장려하는 직장이 되기 위해 노력하고 있습니다. 당사는 다양성이 보장되는 환경에서 직원들이 역량을 발휘하고 우리 고객을 위한 더 좋은 제품을 개발할 수 있다고 믿습니다. 따라서 입사에 관심 있는 사람이 있다면 포지션별 주요 업무와 자격을 확인하고 본인이 보유한 기술과 능력에 부합하는 모든 포지션에 적극적으로 지원하기를 장려합니다. 지원자는 채용 과정에서 역할 관련 평가(해당하는 경우) 및/또는 채용 전 스크리닝을 통과해야 합니다. 자세한 정보는 GM 채용 과정 안내를 참고하십시오.
공평한 취업 기회 선언 (미국)
General Motors는 공평한 기회를 제공하는 고용주임을 자부합니다. 자격을 만족하는 지원자는 인종과 피부색, 성별, 성적 지향, 성별 정체성, 국적, 장애, 재향 군인 보호법 적용 여부와 상관없이 채용 후보로서 심사를 받습니다.
숙소 (미국 및 캐나다)
General Motors는 장애인을 포함한 모든 구직자들에게 취업 기회를 제공합니다. 구직이나 취업 지원에 도움이 되는 합리적인 숙소가 필요한 경우 [email protected]으로 이메일을 보내시거나 800-865-7580으로 전화주십시오. 이메일에, 귀하가 요청하는 특정한 숙소에 대한 설명과 귀하가 지원하는 직무와 채용 요청서 번호를 포함해주세요.