{"id":1803917,"url":"https://alion.io/job/general-motors-technical-lead-manager-sensing-systems-design-validation","title":"Technical Lead Manager, Sensing Systems Design & Validation","company":{"id":1705,"name":"General Motors","domain":"gm.com","url":"https://alion.io/company/general-motors-europe","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":88,"open_postings":74,"ghost_share":0,"stale_share":0.662,"repost_share":0,"time_to_fill_p50_days":12,"computed_at":"2026-10-30T05:48:23Z"}},"role":"Management","role_family":"Management","seniority":"lead","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Sunnyvale, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":189300,"max":290700,"currency":"USD","period":"year","gross":null,"usd_annual":290700},"salary_estimate":null,"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"Data Augmentation","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Synthetic Data","optional":false},{"name":"TensorFlow","optional":false},{"name":"TensorFlow C++","optional":false},{"name":"CI/CD","optional":true},{"name":"Computer Vision","optional":true},{"name":"Google BigQuery","optional":true},{"name":"Linux","optional":true},{"name":"ROS","optional":true},{"name":"SQL","optional":true}],"status":"live","first_seen_at":"2026-10-03T18:54:37Z","employer_posted_date":"2026-10-03","last_verified_at":"2026-10-30T05:37:52Z","board_verified":true,"closed_at":null,"days_open":26,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":26},"description":"Job Description\nWork Arrangement:\nThis 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.\nAbout the Organization\nGeneral Motors is developing advanced driver assistance and autonomous driving systems designed to make personal transportation safer, more accessible, and more capable.\nThe 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.\nWe 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.\nThis 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.\nWhat you’ll do\nSet the technical direction for sensing-system evaluation frameworks that measure performance, robustness, scalability, and readiness across representative driving conditions. \nLead integration of camera, LiDAR, RADAR, and related sensor models into simulation and evaluation environments. Define fidelity, performance, and usability targets for large-scale experimentation. \nBuild 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. \nGenerate 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. \nDesign 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. \nWork 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. \nIntegrate new sensor components and candidate sensing architectures into the autonomous driving software stack. Identify interface, configuration, performance, and maintainability risks across software releases. \nDefine 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. \nDevelop evaluation metrics and reporting that connect sensor performance to system-level requirements, safety objectives, perception outcomes, and architecture trade-offs. \nLead 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. \nIdentify opportunities to improve software architecture, test coverage, data quality, automation, and engineering productivity. Convert lessons learned into repeatable processes and reusable infrastructure. \nMentor engineers and raise the bar for software quality, technical communication, experimental rigor, and ownership. \nHire, coach, develop, and retain a small but high-performing team while maintaining hands-on technical leadership and accountability for delivery. \nYour skills and abilities (required qualifications)\nBachelor’s degree in computer science, computer engineering, electrical engineering, systems engineering, robotics, mathematics, or a related technical field. \nTen or more years of progressive experience in software engineering, systems engineering, machine learning, perception, robotics, autonomous vehicles, or a related field.\nA minimum of 1 year of experience leading a small team of engineers.\nExperience leading complex technical initiatives that span multiple engineering disciplines and require alignment across software, hardware, data, and systems teams. \nExperience designing, building, or integrating simulation, evaluation, validation, or data-processing systems for autonomy, robotics, automotive, aerospace, or another safety-critical domain. \nStrong programming ability in C++ and Python, including production-quality software, testable interfaces, debugging, and automation. \nExperience with machine learning workflows and at least one modern machine learning framework, such as PyTorch or TensorFlow. \nUnderstanding of camera, LiDAR, RADAR, or multimodal sensing systems and the engineering trade-offs that affect their performance. \nExperience using data, statistics, and structured experimentation to evaluate system behavior, identify failure modes, and support technical decisions. \nAbility to communicate complex technical concepts clearly in design reviews, technical documents, presentations, and cross-functional discussions. \nDemonstrated ability to work independently in ambiguous problem spaces, establish priorities, make sound technical judgments, and deliver measurable results. \nExperience working in an iterative software development environment, including code review, testing, version control, and agile planning. \nDemonstrated experience leading, coaching, or managing engineers and creating an inclusive, high-performing team environment. \nWhat can give you a competitive advantage (preferred qualifications)\nMaster’s degree or Ph.D. in computer science, engineering, robotics, mathematics, or a related technical discipline. \nDirect experience developing or evaluating perception and machine learning models for autonomous vehicles, advanced driver assistance systems, mobile robots, or other safety-critical systems. \nExperience modeling or characterizing sensor degradation caused by rain, snow, fog, aerosols, dirt, water, glare, low light, occlusion, or other adverse conditions. \nExperience defining sensing architectures or sensor performance requirements for autonomous vehicle platforms. \nExperience with synthetic data generation, scenario-based testing, data replay, sensor simulation, simulation-to-real correlation, or large-scale regression evaluation. \nExperience with sensor calibration, geometric computer vision, multimodal fusion, signal quality, timing synchronization, or sensor health monitoring. \nExperience with verification and validation methods for safety-critical systems, including systems engineering, requirements traceability, functional safety, or Safety of the Intended Functionality (SOTIF). \nExperience with Robot Operating System (ROS) or similar robotics middleware, Linux, containers, continuous integration, distributed computing, or cloud-based data platforms. \nExperience building metrics, dashboards, experiment-management tools, or data pipelines that help technical leaders make timely decisions. \nExperience with SQL / BigQuery, data visualization, statistical analysis, or scalable processing of large sensor and vehicle datasets. \nExperience partnering with suppliers, research organizations, or external technology providers on sensing, simulation, or autonomy capabilities. \nExperience mentoring senior engineers, leading technical communities, or setting architecture direction across multiple teams. \nWorking style and expectations\nYou put safety, quality, data integrity, and engineering rigor at the center of technical decisions. \nYou balance first-principles reasoning with empirical evidence from simulation, recorded data, and vehicle testing. \nYou are comfortable moving between architecture, software implementation, data analysis, and cross-functional leadership. \nYou make trade-offs explicit and communicate assumptions, risks, limitations, and next steps. \nYou help teams move quickly without weakening validation discipline or long-term maintainability. \nYou contribute to a respectful, inclusive environment where people can do their best work. \nCompensation: 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 the California Bay Area.\nThe salary range for this role is ($189,300 - $290,700). The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position. \nBonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance. \nCompany Vehicle: Upon successful completion of a motor vehicle report review, you will be eligible to participate in a company vehicle evaluation program, through which you will be assigned a General Motors vehicle to drive and evaluate. Note: program participants are required to purchase/lease a qualifying GM vehicle every four years unless one of a limited number of exceptions applies.\nThis Job may be eligible for relocation benefits.\n\nAbout GM\nOur vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all.\nWhy Join Us\nWe believe we all must make a choice every day - individually and collectively - to drive meaningful change through our words, our deeds and our culture. Every day, we want every employee to feel they belong to one General Motors team.\nBenefits Overview\nFrom day one, we're looking out for your well-being-at work and at home-so you can focus on realizing your ambitions. Learn how GM supports a rewarding career that rewards you personally by visiting Total Rewards resources.\nNon-Discrimination and Equal Employment Opportunities (U.S.)\nGeneral Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers.\nAll employment decisions are made on a non-discriminatory basis without regard to sex, race, color, national origin, citizenship status, religion, age, disability, pregnancy or maternity status, sexual orientation, gender identity, status as a veteran or protected veteran, or any other similarly protected status in accordance with federal, state and local laws.\nWe encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role-related assessment(s) and/or a pre-employment screening prior to beginning employment. To learn more, visit How we Hire.\nAccommodations\nGeneral Motors offers opportunities to all job seekers including individuals with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, email us or call us at 1-800-865-7580. 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