Technical Lead Manager, Sensing Systems Design & Validation

Job Description Work Arrangement: This role is categorized as Remote/ hybrid . Remote: This role is based remotely but if you live within a 50-mile radius of [Austin, Detroit, Seattle, Warren, Milford, Sunnyvale  ], you are expected to report to that location three times per week, at minimum. About the Organization General Motors is developing advanced driver assistance and autonomous driving systems designed to make personal transportation safer, more accessible, and more capable. The Autonomous Vehicle Architecture and System Design team defines the technical foundations that allow sensing, perception, machine learning, simulation, and vehicle software to work together reliably across real-world conditions. We are looking for an experienced Technical Lead Manager to lead the architecture and development of sensing-system evaluation capabilities. You will define how camera, LiDAR, and RADAR systems are characterized, simulated, integrated, and validated across the operational design domain. You will combine statistical analysis, empirical evidence, first-principles engineering, and software development to guide sensor architecture decisions and accelerate the delivery of safe, scalable autonomy systems. This role offers broad technical influence. You will work across sensing, perception, machine learning, simulation, vehicle integration, data, safety, and program teams. You will also create the tools, workflows, and technical alignment needed to turn large-scale experiments into clear engineering decisions. What you’ll do Set the technical direction for sensing-system evaluation frameworks that measure performance, robustness, scalability, and readiness across representative driving conditions. Lead integration of camera, LiDAR, RADAR, and related sensor models into simulation and evaluation environments. Define fidelity, performance, and usability targets for large-scale experimentation. Build statistical and empirical methods that complement physics-based analysis. Use experiment design, confidence measures, failure analysis, and trend analysis to characterize sensor and perception performance. Generate and manage synthetic data at scale from road-data replay, simulation, and scenario-generation pipelines. Partner with sensing and perception engineers to translate requirements into useful datasets. Design data augmentation and scenario coverage for challenging conditions such as nighttime, rain, snow, fog, sensor contamination, sensor cleaning, occlusion, glare, and other sources of performance degradation. Work with machine learning and perception teams to make synthetic and recorded datasets suitable for model training, evaluation, regression testing, and failure analysis. Lead initial model retraining or pipeline demonstrations when needed to reduce end-to-end technical risk. Integrate new sensor components and candidate sensing architectures into the autonomous driving software stack. Identify interface, configuration, performance, and maintainability risks across software releases. Define and improve developer tools for running sensing experiments, including application programming interfaces, command-line tools, notebooks, automated workflows, and dashboards that communicate results to technical and program leaders. Develop evaluation metrics and reporting that connect sensor performance to system-level requirements, safety objectives, perception outcomes, and architecture trade-offs. Lead technical reviews and cross-domain initiatives from ambiguous requirements through design, implementation, validation, and adoption. Build alignment across teams with different objectives, constraints, and levels of technical detail. Identify opportunities to improve software architecture, test coverage, data quality, automation, and engineering productivity. Convert lessons learned into repeatable processes and reusable infrastructure. Mentor engineers and raise the bar for software quality, technical communication, experimental rigor, and ownership. Hire, coach, develop, and retain a small but high-performing team while maintaining hands-on technical leadership and accountability for delivery. Your skills and abilities (required qualifications) Bachelor’s degree in computer science, computer engineering, electrical engineering, systems engineering, robotics, mathematics, or a related technical field. Ten or more years of progressive experience in software engineering, systems engineering, machine learning, perception, robotics, autonomous vehicles, or a related field. A minimum of 1 year of experience leading a small team of engineers. Experience leading complex technical initiatives that span multiple engineering disciplines and require alignment across software, hardware, data, and systems teams. Experience designing, building, or integrating simulation, evaluation, validation, or data-processing systems for autonomy, robotics, automotive, aerospace, or another safety-critical domain. Strong programming ability in C++ and Python, including production-quality software, testable interfaces, debugging, and automation. Experience with machine learning workflows and at least one modern machine learning framework, such as PyTorch or TensorFlow. Understanding of camera, LiDAR, RADAR, or multimodal sensing systems and the engineering trade-offs that affect their performance. Experience using data, statistics, and structured experimentation to evaluate system behavior, identify failure modes, and support technical decisions. Ability to communicate complex technical concepts clearly in design reviews, technical documents, presentations, and cross-functional discussions. Demonstrated ability to work independently in ambiguous problem spaces, establish priorities, make sound technical judgments, and deliver measurable results. Experience working in an iterative software development environment, including code review, testing, version control, and agile planning. Demonstrated experience leading, coaching, or managing engineers and creating an inclusive, high-performing team environment. What can give you a competitive advantage (preferred qualifications) Master’s degree or Ph.D. in computer science, engineering, robotics, mathematics, or a related technical discipline. Direct experience developing or evaluating perception and machine learning models for autonomous vehicles, advanced driver assistance systems, mobile robots, or other safety-critical systems. Experience modeling or characterizing sensor degradation caused by rain, snow, fog, aerosols, dirt, water, glare, low light, occlusion, or other adverse conditions. Experience defining sensing architectures or sensor performance requirements for autonomous vehicle platforms. Experience with synthetic data generation, scenario-based testing, data replay, sensor simulation, simulation-to-real correlation, or large-scale regression evaluation. Experience with sensor calibration, geometric computer vision, multimodal fusion, signal quality, timing synchronization, or sensor health monitoring. Experience with verification and validation methods for safety-critical systems, including systems engineering, requirements traceability, functional safety, or Safety of the Intended Functionality (SOTIF). Experience with Robot Operating System (ROS) or similar robotics middleware, Linux, containers, continuous integration, distributed computing, or cloud-based data platforms. Experience building metrics, dashboards, experiment-management tools, or data pipelines that help technical leaders make timely decisions. Experience with SQL / BigQuery, data visualization, statistical analysis, or scalable processing of large sensor and vehicle datasets. Experience partnering with suppliers, research organizations, or external technology providers on sensing, simulation, or autonomy capabilities. Experience mentoring senior engineers, leading technical communities, or setting architecture direction across multiple teams. Work