In - Vehicle Network Architect
Location: Dearborn, MI, United States
We are the movers of the world and the makers of the future. We get up every day, roll up our sleeves and build a better world -- together. At Ford, we’re all a part of something bigger than ourselves. Are you ready to change the way the world moves?
In this position...
Join Ford as an In-Vehicle Network Performance Architect. In this individual contributor role, you will own the vision, standards, and operating model for network performance modeling and continuous performance insight across complex Ethernet/CAN-FD/LIN vehicle networks. You will build and maintain high-fidelity models to predict latency/jitter/utilization and coupling, run data-driven studies that shape architecture decisions, forecast lifecycle growth, and use SIL/HIL/vehicle data pipelines and dashboards to detect and respond to emerging performance risks during development
Ford’s Network Architecture organization is advancing faster, data-driven end-to-end network thinking that unifies software, hardware, and platform programs. As the In-Vehicle Network Performance Architect, you will act as the technical product owner for Ford’s network performance modeling and monitoring capability—defining “what good looks like,” standardizing methods, and ensuring the right performance insights are produced at the right time in the systems engineering and architecture lifecycle.
This role blends architecture/systems engineering judgment with real-world complex network performance analysis. The ideal background may come from automotive or adjacent domains (defense, aerospace, networking/IT, distributed systems), but must include experience with vehicle communications (Ethernet, CAN/CAN-FD, LIN).
Join Ford as an In-Vehicle Network Performance Architect. In this individual contributor role, you will own the vision, standards, and operating model for network performance modeling and continuous performance insight across complex Ethernet/CAN-FD/LIN vehicle networks. You will build and maintain high-fidelity models to predict latency/jitter/utilization and coupling, run data-driven studies that shape architecture decisions, forecast lifecycle growth, and use SIL/HIL/vehicle data pipelines and dashboards to detect and respond to emerging performance risks during development
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