Staff Vehicle Architecture Engineer at Rivian and Volkswagen Group Technologies in Irvine, CA

Job description

About Us Rivian and Volkswagen Group Technologies is a joint venture between two industry leaders with a clear vision for automotive’s next chapter. From operating systems to zonal controllers to cloud and connectivity solutions, we’re addressing the challenges of electric vehicles through technology that will set the standards for software-defined vehicles around the world. The road to the future is uncharted. By combining our expertise across connectivity, AI, security and more, we’ll map a new way forward. Working together, we’ll create a future that’s more connected, more intelligent, more sustainable for everyone. Role Summary The Vehicle System Architecture team owns the vehicle-level mechatronic architecture at Rivian VW Group Technologies. We translate product intent from Brand Product Definition, Program, Cost Engineering, Manufacturing, Strategy and Reliability into parts and function distribution across the vehicle — from product concept through manufacturing. Responsibilities Architecture Modeling and Trade Studies • Build and own the parametric models behind vehicle architecture decisions: controller count and placement, functional allocation across zonal and secondary ECUs, sensor and actuator distribution, harness topology. • Run architecture trade studies with quantified outcomes — cost, mass, harness complexity, power, temperature and environmental, reliability, manufacturability — and ship each study with its model, parameters and sensitivity analysis. • Build the cost and BOM models that turn architecture options into numbers • Support cost-down by subtraction: use the models to show which controllers, harness runs and sensors the vehicle can do without, before anyone optimizes what is left. • Analyze new technology integration with SW HW platform design approach Applied AI for Architecture Work • Build LLM and agentic pipelines over architecture artifacts: extracting device and interface data from specifications, generating and checking candidate architecture configurations, and surfacing gaps across requirements, device lists and interface definitions. • Deploy semantic search over requirements, architecture models, device data and prior trade studies • Integrate AI assistance into the team's toolchain — Flow Engineering, Airtable, GitLab — via APIs and CI pipelines, with human-in-the-loop guardrails. • Build analytics that connect architecture decisions to what happened downstream: field issues, manufacturing escapes, cost variance — so the next trade study starts better informed, Systems Engineering, Hands-On • Contribute to vehicle-level STPA, DFMEA and fault analyses, and feed findings back into the architecture and its models. • Participate in architecture, interface and safety design reviews across domains. • Correlate models against real vehicle, lab rig and supplier data • Document the methods you build as playbooks other engineers can run. Qualifications Minimum Qualifications: • BS/MS in Electrical, Computer, Mechanical, Systems or Aerospace Engineering, or equivalent demonstrated experience. • 5+ years in vehicle, aerospace or electromechanical systems engineering — or an equivalent record of building the analyses that decided real designs. • Strong Python — data processing, parametric modeling, automation, prototyping AI workflows. • Practical experience building LLM applications: RAG pipelines, semantic search, structured extraction or agent frameworks. • Systems-minded: able to decompose a vehicle into subsystems, functions and interfaces, and reason about how a change in one propagates to the others. • Comfortable with numbers that carry consequences — cost, mass, power budgets — and with defending them. Preferred Qualifications • E/E architecture background: zonal and secondary ECUs, LV power distribution, networks, harness design, CAN/Ethernet, DBC/ARXML. • Physical simulation tools: OpenModelica, Julia, Spice, Modelica, or FMI/FMU co-simulation. • Worst-case, tolerance and statistical ana