Senior AV Platform Verification Engineer

General Motors

United States

On-site

USD 115,000 - 155,000

Full time

14 days+

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Job summary

General Motors is seeking an experienced ADAS/AV Platform Software Verification Engineer to define and optimize system testing for ADAS/AV platform software. You will design scalable test frameworks, analyze diagnostics, and collaborate with cross-functional teams to ensure quality and safety across vehicle systems.

Responsibilities include building HIL/SIL/VIL environments, conducting root-cause analyses, and driving automated test execution to improve validation efficiency.

Qualifications

  • Bachelor’s degree in electronics, computer science, electrical engineering or a related technical degree.
  • Minimum of 6 years of professional technical experience.
  • Minimum of 3 years of Software Verification and Validation experience.
  • Proficient in Python, CAPL, Vehicle Spy, and Robot Framework.

Responsibilities

  • Design and develop scalable, high-coverage software testing for the ADAS/AV platform.
  • Analyze diagnostics to support triaging and debugging for ADAS/AV platform software.
  • Build and maintain HIL, SIL, and VIL testing environments.
  • Ensure compliance with ISO 26262, SOTIF, FMVSS 111, and FMVSS 127 while improving test coverage.
  • Collaborate with Software Infra and application teams on test cases and middleware.

Skills

Python
CAPL
Vehicle Spy
Robot Framework
CAN
Ethernet

Education

Bachelor’s degree in electronics, computer science, electrical engineering or related technical degree

Tools

Wireshark
CANoe/CANalyzer
INCA
RAD Galaxy RAD Gigastar

Job description

General Motors is seeking an experienced ADAS/AV Platform Software Verification Engineer to define and optimize system testing for ADAS/AV platform software. You will design scalable test frameworks, analyze diagnostics, and collaborate with cross-functional teams to ensure quality and safety across vehicle systems.

Responsibilities include building HIL/SIL/VIL environments, conducting root-cause analyses, and driving automated test execution to improve validation efficiency.

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