In-vehicle Network Analysis Tools Engineer

Ford Ford · Auto · Allen Park, MI +1 · PD Operations and Quality

This role focuses on developing software tools and simulation models for in-vehicle network analysis, performance tracking, and impact prediction. The engineer will define key performance indicators (KPIs), integrate analysis into CI/CD pipelines, and collaborate with architects to validate network-related decisions. The goal is to ensure the scalability and stability of next-generation vehicle architectures by providing data-driven insights into network performance.

What you'd actually do

  1. Design and implement scalable and composable software solutions for in-vehicle network bandwidth analysis, message latency tracking, and functional coupling measurement.
  2. Research, define, and implement a relevant set of KPIs for network modeling, performance analysis, and change management to proactively identify risks and opportunities.
  3. Develop and maintain sophisticated network simulation models to conduct 'what-if' analysis, proactively predicting the impact of new features and architectural changes on bandwidth, latency, and overall performance.
  4. Translating complex network analysis results into clear, concise reports and dashboards for architects, feature owners, and executive leadership to guide strategic decisions.
  5. Integrate network analysis tools and KPIs into the CI/CD pipeline to provide continuous feedback on the health and performance of the vehicle network throughout the development lifecycle.

Skills

Required

  • Software engineering
  • Data analysis
  • Tool development
  • Back-end systems
  • Network architecture
  • Network protocols
  • Communication standards (Ethernet, CAN, LIN)
  • Test-Driven Development (TDD)
  • Gherkin
  • Developing analytical tools
  • Defining metrics (KPIs)
  • Python
  • C++
  • Java

Nice to have

  • Master’s degree
  • Network Analysis and Simulation tools
  • Tools development for Embedded Systems
  • Data science
  • Statistical analysis
  • Machine learning techniques
  • Autosar Classic and Adaptive architecture
  • Industry standard formats (DBC, ARXML)

What the JD emphasized

  • Software Developer first
  • 2+ years of work experience in software engineering
  • 2+ years of experience applying knowledge of network architecture, protocols, and communication standards
  • 2+ years of experience in test-Driven Development (TDD) with Gherkin
  • 2+ years experience in developing analytical tools and defining metrics (KPIs)
  • 2+ years of experience programming in languages such as Python, C++, or Java