Virtualization Engineer \u2013 SIL / Virtual ECU (Automotive)

Stellantis NV

  • Auburn Hills, MI
  • 25 days ago

    Highlights

    We are seeking a Virtualization Engineer to develop and scale Software-in-the-Loop (SIL) and Virtual ECU environments that enable earlier integration, faster regression testing, and higher-quality software releases. Strong systems thinking, debugging skills, and experience integrating virtual ECUs with virtual networks/interfaces.

    Numbers & Facts

    LocationAuburn Hills, MI

    Description

    About the Role

    We are seeking a Virtualization Engineer to develop and scale Software-in-the-Loop (SIL) and Virtual ECU environments that enable earlier integration, faster regression testing, and higher-quality software releases. This role supports software-defined vehicle development and propulsion control systems innovation.

    Key Responsibilities

    • Design, build, andmaintainSoft ECU / Virtual ECU solutions for propulsion controllers

    • Develop,operate, andoptimize SIL environments including integration, execution, and reporting

    • Automate virtual testing workflows including build support, execution, and regression reporting

    • Troubleshoot integration issues across toolchains, interfaces, and models

    • Partner with software, controls, systems, and validation teams to define requirements and increase adoption

    Basic Qualifications:

    • Bachelor's degree in Electrical, Computer, Mechanical Engineering, Computer Science, or related field.

    • 5+ years in automotive/embedded virtualization (MIL/SIL/HIL, ECU virtualization).

    • Experience with virtualization/co-simulation tools (dSPACE/ETAS/Vector or similar).

    • Strong systems thinking, debugging skills, and experience integrating virtual ECUs with virtual networks/interfaces.

    • Experience working cross-functionally in an Agile delivery environment.

    Preferred Qualifications:

    • Propulsion or electrified powertrain experience.

    • Familiarity with Linux and scripting (Python), Git-based workflows, and automated regression practices.

    • CAN/LIN/Ethernet concepts and related debugging/analysis tools.

    • Simulink/MBD, FMI/FMU, plant modeling exposure.

    • CI/CD tools and Docker familiarity.

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