Über
Lead Machine Learning Validation Engineer , you will spearhead innovative machine learning research aimed at enhancing the verification and validation of ML components utilized in robotics and autonomous driving systems. Your focus will be on simulation-based evaluation, uncertainty modeling, scenario coverage automation, and converting advanced ML concepts into functional prototypes that significantly improve the efficiency, accuracy, and coverage of ML system validation. Key Responsibilities Prototype groundbreaking research concepts into efficient tools integrated into CI/CD and extensive validation pipelines. Enhance machine learning research for both open and closed loop simulation validation. Develop tooling for scenario generation, coverage-guided testing, and rare-event discovery. Create robust metrics and methods for uncertainty and Out-of-Distribution detection tailored for autonomous ML systems. Evaluate deep learning models across perception, prediction, and planning using realistic sensor and traffic simulations. Improve behavioral coverage and hazard-aligned metrics critical for making release readiness decisions. Collaborate effectively with Simulation, Safety, Systems Engineering, and cross-functional teams. Author technical documentation, white papers, and contribute to the development of validation methodology standards. Research Focus Areas Scenario synthesis (including diffusion models, generative models, counterfactuals). Coverage-based and fuzzing-based evaluation for autonomous behavior. Uncertainty estimation, calibration, conformal prediction, and Out-of-Distribution detection. Robustness testing and perturbation frameworks. Test suite prioritization, failure mining, and regression analysis. Required Qualifications MS + 5 years or PhD + 3 years in ML, Robotics, Computer Science, or a related field. Proven experience with simulation-driven ML evaluation for robotics and autonomy. Strong proficiency in Python and ML frameworks like PyTorch, JAX, or TensorFlow. Demonstrated capability to transform complex ML ideas into working prototypes. Experience in integrating ML evaluation into CI/CD processes. Evidence of impactful research through published work, internal tools, or patents. Excellent communication skills with an aptitude for cross-functional collaboration. Preferred Qualifications Experience with autonomy stacks (including perception, prediction, and planning). Familiarity with simulation platforms such as CARLA, SVL, DriveSim, or Applied Intuition. Knowledge of Bayesian ML, causal inference, and sequential testing methods. Experience working with digital twin systems and sensor simulation. Understanding of automotive safety standards including ISO 26262, UL 4600, and SOTIF. Experience creating validation dashboards and scorecards linked to release criteria. Success Criteria More rapid detection of ML regressions with enhanced test efficiency. Refined uncertainty and robustness metrics that aid in release decisions. Working prototypes integrated into production validation workflows. Measurable contributions to simulation strategies, hazard coverage, and ML confidence scoring. Compensation:
The salary range for this role is $144,700 - $261,300, dependent on various factors relevant to the position. Additionally, an incentive pay program offers payouts based on company performance, job level, and individual contributions. Benefits:
GM provides an array of health and wellness benefits, including medical, dental, vision plans, Health Savings Accounts, Flexible Spending Accounts, retirement plans, life insurance, and paid time off among others. Enjoy discounts on GM vehicles and much more. About GM We are driven by our vision of achieving Zero Crashes, Zero Emissions, and Zero Congestion, and we accept the responsibility to lead the transformation that will improve our world for everyone. Why Join Us We strive to foster an environment where every employee feels like a valued member of the General Motors team, contributing to meaningful change through our actions and culture.
Sprachkenntnisse
- English
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