Senior Software Systems Engineer, L3 and L4 – Autonomous Driving

Systems EngineerSystems EngineerFull TimeRemoteTeam 10,001+Since 1993H1B SponsorCompany SiteLinkedIn

Location

California

Posted

13 days ago

Salary

$184K - $287.5K / year

Postgraduate Degree8 yrs expExperience acceptedEnglish

Job Description

• Develop use cases and system requirements for L3 and L4 autonomous driving products based on customer needs, traffic regulations, certification standards, and industry guidelines • Perform system-level analysis to define performance requirements and allocate performance budgets across subsystems • Formulate test cases and define critical performance metrics to ensure compliance with functional and safety requirements • Define and drive online and offline test strategy and execution at both vehicle and component levels • Partner closely with Data Analytics, Test Engineering, and System Integration & Test teams • Ensure the right evaluators and important metrics are developed • Prove that datasets cover sufficient scenarios and requirements • Target appropriate sampling strategies through Data Collection and Real-World Driving • Establish strong multi-functional relationships with collaborators across engineering, safety, validation, and product teams • Drive innovation in requirements decomposition, traceability, and verification processes • Promote and cultivate a strong systems engineering approach across the organization

Job Requirements

  • MS or PhD in Engineering, Physics, Computer Science, or a related field (or equivalent experience)
  • 8+ years proven experience in safety-critical systems engineering, system analysis, data analysis, and software architecture
  • Strong software development background with proven coding skills
  • Hands-on experience in SOTIF analysis (ISO 21448), functional safety (ISO 26262), and multi-functional architectural trade-off analysis
  • Strong leadership and interpersonal skills, with the ability to drive alignment across large organizations
  • Proven understanding of trade-offs between End-to-End deep learning approaches, classical modular perception/planning stacks, and associated validation and test strategies

Benefits

  • equity
  • benefits

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