Join our team as a Custom Software Engineer and contribute to developing innovative software solutions. This role focuses on designing, coding, and enhancing system and application components using modern frameworks and agile practices. You will deliver scalable, high-performing solutions tailored to specific business needs, ensuring quality and efficiency.

This position offers an exciting opportunity to work on cutting-edge technology within the automotive sector. You will collaborate with cross-functional teams to drive project success and meet evolving client expectations in a dynamic environment.

Develop comprehensive test plans and specifications for advanced driver-assistance systems (ADAS) features, including Lane Departure Warning (LDW), Lane Keeping Assist (LKA), Adaptive Cruise Control (ACC), Autonomous Emergency Braking (AEB), Forward Collision Warning (FCW), Blind Spot Detection (BSD), and Rear Cross Traffic Warning (RCW), based on system and software requirements.

Design and execute validation campaigns across Model-in-the-Loop (MIL), Software-in-the-Loop (SIL), and Hardware-in-the-Loop (HIL) environments for ADAS algorithms and Electronic Control Unit (ECU) testing. Build, configure, and maintain HIL test benches using platforms like dSPACE, SCALEXION, Micro Auto Box, or Vector Canoe RT for sensor simulation and actuator emulation.

Develop MATLAB Simulink-based test harnesses for model-based verification and automated regression testing of ADAS functions. Implement and maintain automated test frameworks using Python, CAPL, or Robot Framework, integrated with Continuous Integration/Continuous Deployment (CI/CD) pipelines. Validate sensor fusion and perception outputs, assessing object detection accuracy, tracking performance, false positive/negative rates, and robustness across diverse Operational Design Domain (ODD) scenarios.

Perform fault injection and negative testing to verify ISO 26262 ASIL B/C safety mechanisms, covering sensor, actuator, and communication failure conditions. Analyze test results, manage defect reporting in JIRA/ALM, and maintain end-to-end requirements traceability using tools such as Polarion, CodeBeamer, or IBM DOORS NG. Support ASPICE SWE4, SWE5, SWE6, SYS4, and SYS5 compliance activities through verification and validation documentation.

Participate in vehicle-level ADAS validation, including drive test data analysis, ground truth annotation, performance assessment, and benchmarking against New Car Assessment Program (NCAP) requirements.

A Bachelor's or Master's degree in Electronics, Electrical, Computer Science, Mechatronics, or Automotive Engineering is required, with a minimum of 15 years of full-time education. Candidates must possess a minimum of 3 years of experience in Automotive ECU Software.

Key technical proficiencies include strong experience in ADAS Verification and Validation across MIL, SIL, and HIL environments, with hands-on expertise in HIL platforms such as dSPACE, SCALEXION, Micro Auto Box, NI VeriStand, or Vector Canoe RT. Proficiency in MATLAB Simulink for model-based testing, test automation, and regression framework development is essential.

Solid understanding of ISO 26262 software and system testing requirements for ASIL B/C safety-critical systems is necessary. Experience with ADAS data logging and analysis tools like ADTF, DLT Viewer, and Rosbag, along with knowledge of SOTIF (ISO 21448) and scenario-based validation approaches for ADAS feature verification, is beneficial.

Experience with requirements management and traceability tools including Polarion, CodeBeamer, IBM DOORS NG, or Jama Connect is expected. Familiarity with Open SCENARIO and Open DRIVE standards for scenario generation and validation, as well as exposure to virtual validation environments such as CARLA, IPG Car Maker, or rFpro, is advantageous.

Understanding of Euro NCAP and NHTSA ADAS validation protocols, including AEB, LKA, and AES test scenarios, is important. Working knowledge of Python-based data analysis libraries like Pandas, NumPy, and Matplotlib for test result evaluation, along with experience in CAN and CAN FD network analysis using tools such as Vector CANalyzer or Canoe, is required.