Controller Modelling Developer – Virtual Prototyping

Work Arrangement:

Hybrid : This role is categorized as hybrid. This means the successful candidate is expected to report to Markham Elevation Centre (MEC) three times per week, at minimum.

Introduction:

The Virtualization and SIL Integration team at General Motors is a highly technical, highly knowledgeable, passionate, strategic, and visionary group working on an organizational transformation. We are seeking cutting-edge solutions to the question: how do we develop and validate our embedded control system without any hardware? Part of developing a completely virtual embedded control system is developing L4 virtual electronic control units (vECU) that serve as real-time virtual counterparts of physical ECUs. These are functional virtual prototypes of ECUS that execute unmodified production code. These enable engineers to develop software years before ECU hardware is available.

The General Motors Virtualization and SIL Integration team is expanding its capabilities to model, integrate, and fully develop Level 4 Virtual ECUs. As a Virtual Prototyping Application Engineer, you will be responsible for making SystemC TLM 2.0 models of ECU microcontrollers and peripherals. This team supports all vehicle embedded platform features, including Electrification, Propulsion, Motion Controls, Chassis, and Body modules.

Responsibilities

• Create SystemC TLM2.0 models of microcontrollers, asics, drivers, modules, switches, timers, transceivers, etc.

• Integrate microcontroller and peripheral models into full Level 4 virtual ECUs

• Debug model and integration bugs and issues

• Work cross-functionally with Design Release Engineers, Tier 1 suppliers, and software engineers to develop vECUs

• Integrate production software into vECUs

• Validate and verify vECU functions as expected and executes production software as intended

• Collaborate with software developers to root cause and solve low-level, basic software and application software issues found in the virtual environment

• Debug integrated production software as needed

• Develop script-based engineering workarounds to bridge virtual model limitation gaps

• Work cross-functionally with Simulation Integration Engineers to support the integration of vECUs into virtual environments

Qualifications – Required

• Minimum 10+ years of relevant professional engineering experience

• Proficiency in SystemC, Transaction Level Modeling (TLM 2.0), and SCML

• Expertise in creating Level 4 VECU prototypes

• Proficiency in off the shelf EDA toolchains such as Synopsys Virtualizer and ASTC vLAB

• Expertise in various programming languages (C, C++, Python)

• Expertise in low-level input/output driver development and debugging

• Knowledge of electronic control modules and embedded control systems

• Knowledge of ARM based microcontrollers

• Software integration, compiler and linker file comprehension expertise

• Knowledge of AUTOSAR and Automotive Cyber Security

• Knowledge in automotive communications (e.g. CAN, LIN, SPI, I2C, UART)

• Co-Simulation expertise of Level 4 VECUs

• Expertise in the use of software debugging tools (Lauterbach, Green Hills Multi IDE, etc.)

• Expertise in Intrepid Vehicle Spy, Vector CANoe

• Excellent communication skills

• Demonstrated success in leading challenging projects with high levels of ambiguity to completion

• Consistent demonstration of GM leadership behavioral competencies

• HW architecture/micro-architecture experience.

Qualifications - Preferred

• Bachelor’s or Master’s degree in Electrical Engineering, Computer Engineering, Software Engineering or Computer Science or similar field.

• Knowledge of software test automation

• Expertise in software tools development Software tools development expertise

• Embedded control system design expertise

• Production vehicle program execution experience

• Development experience in both Windows and Linux environments

• Familiarity with software development build process and associated tools (e.g. Gerrit, Jenkins, Git, etc.)

• Familiarity with scalable simulation deployment methodologies leveraging containers (e.g. Docker, Singularity, etc.)

• Familiarity with synchronous and asynchronous event driven simulation