Systems Integration Engineer, Diagnostics - Automotive
About the role
Key Responsibilities · Diagnostic Algorithm Design & Implementation: Architect and implement real-time diagnostic monitors in modern C++, including signal-level plausibility checks, degradation detection, and fault isolation logic. Perform software unit testing, HIL testing, and automated regression tests to validate diagnostic algorithms prior to final implementation · Diagnostic Requirement Engineering: Author formal diagnostic monitor requirements (detection thresholds, debounce strategies, fault response actions) traceable to system-level or component level failure modes; verify requirements against fleet and bench data. · Data-Driven Analysis & Validation: Leverage large-scale fleet data in Data bricks to characterize faults, tune detection thresholds, validate algorithm performance (detection rate, false-positive rate), and drive continuous improvement. · Fault Troubleshooting & Root Cause Analysis: Lead investigation of field-reported faults using telemetry, logged data, and replay tools; identify root causes spanning hardware, firmware, calibration, and environmental factors. · Documentation & Knowledge Transfer: Produce clear technical specifications, design documents, and service procedures that enable Fleet Operations technicians to diagnose and resolve faults efficiently. · Continuous Improvement: Monitor fleet-wide diagnostic KPIs; propose and implement algorithm refinements, new monitors, and process improvements. Qualifications · Education: Bachelor's degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, Robotics, Aerospace Engineering, or a related technical field. Master's degree preferred · Experience: 6–8 years of professional experience in diagnostics, embedded controls, or vehicle systems engineering within the automotive, autonomous vehicle, aerospace, or robotics industry. Required Skills · Vehicle Diagnostics Expertise: Working knowledge of diagnostic standards and protocols (UDS / ISO 14229, DTC management, OBD-II / SAE J1979) and communication buses (CAN, LIN, Automotive Ethernet). · Data Analysis: Proficiency with Python for data analysis and visualization; experience working with large-scale telemetry datasets (Data bricks, or equivalent). · C++ Proficiency: Strong, demonstrable C++ development skills in production or safety-critical environments; · Control & Estimation Theory: Hands-on experience designing and implementing control algorithms, state estimators, or model-based diagnostic observers. · Problem Solving: Proven ability to systematically decompose complex, multi-domain problems and drive them to root cause under ambiguity. · Communication: Excellent written and verbal communication skills; able to present technical analyses and recommendations to both engineering peers and non-technical stakeholders. Preferred Skills · Experience with implementing prognostics or vehicle health monitoring Experience with HIL/SIL testing frameworks for diagnostic validation.