Graphic Design Jobs 2026 (Now Hiring) – Smart Auto Apply

In this role, you will:

• Establish and refine the system/component-level targets for reliability performance of the sensors (including LiDAR, radar, and camera components), AI compute system, and other automotive electronic control units (ECU) in collaboration with internal stakeholders on the Hardware and Sensors Engineering teams.
• Drive the design failure mode and effects analysis process (DFMEA) for relevant sensors, high-performance computers, and ECUs to capture key reliability risks and define appropriate mitigation strategies.
• Use reliability targets, DFMEA outputs, and physics-of-failure principles to partner with validation engineers in developing virtual and physical test plans that prove out designs and demonstrate required reliability performance.
• Lead the definition and deployment of Prognostics and Health Monitoring (PHM) strategies for sensors, compute, and EE systems, including identification of available signals, development of health indicators, degradation models, and failure precursors to enable early fault detection and remaining useful life estimation
• Partner with software, data, and systems teams to operationalize PHM capabilities in the vehicle and backend pipelines, translating reliability risks into actionable monitoring, alerting, and maintenance recommendations.
• Pave the way from development to field deployment by building closed-loop reliability systems that leverage field data, PHM insights, and fleet telemetry to identify performance improvement opportunities and drive corrective actions across design, validation, and operations.

Qualifications

• Bachelor's and/or Master’s-level engineering degree or equivalent technical background with 3-5 years of hands-on experience in Reliability Engineering.
• Detailed understanding of sensors, high-performance computers, and ECUs, including common failure modes and associated validation methodologies.
• Expertise in reliability data analysis, risk assessment, and development of component reliability targets aligned with functional safety and business objectives.
• Strong foundation in reliability statistics (e.g., Weibull, life data analysis, degradation modeling, confidence bounds) and reliability physics (e.g., thermal, vibration, electrical, and environmental failure mechanisms).
• Experience in failure mode assessment, accelerated reliability testing, advanced field reliability monitoring, and/or prognostics and health management concepts.
• Personable, with the ability to lead and influence cross-functional engineering teams toward world-class reliability and dependability.

Bonus Qualifications

• Demonstrated knowledge in Python-based data analysis tools such as PySpark, Pandas, NumPy, and SciPy.
• ASQ Certified Reliability Engineer, or similar professional recognition
• An understanding of ISO 26262 Functional Safety