Sr. Vehicle Modeling Engineer, Applied AI Systems
Irvine, California, United StatesAll locationsIrvine, California, United StatesPalo Alto, California, United States Hybrid
$135,100–$185,800 a yearJobFig found this opening at its original source and checks that it remains available.
About the role
The Systems Design Reliability Engineering (SDRE) team is building the next generation of AI-assisted, model-driven systems engineering at Rivian VW Group — replacing heavyweight requirements processes with simulation-first design, Digital Twin-based verification and test coverage. We are a small, high-leverage team, and we are looking for an engineer who wants to work at the intersection of AI tooling and physical system modelling. You will develop and operate the AI tooling and Digital Twin infrastructure that underpins SDRE's cross-domain methods — across Vehicle Controls, Infotainment, Communications, and Access. You will own feature(s) end-to-end: from building plant models and co-simulation environments, to deploying LLM-assisted requirement and test pipelines. You will also do real systems engineering — author requirements, perform analyses, design reviews, STPA, and apply SDRE methods hands-on.
What you'll bring
- BS/MS in Electrical, Computer, Mechanical, or Systems Engineering, or related field — or equivalent demonstrated experience through projects.
- Strong Python skills — data processing, prototyping AI workflows, automation scripts, or microservices.
- Practical experience building LLM applications: RAG pipelines, semantic search, structured reasoning, or agent frameworks.
- Systems-minded: able to decompose a physical product into subsystems, behaviors and interfaces — and reason about how they interact.
- Comfort iterating quickly from prototype, to production, using modelling in a fast-paced engineering environment.
- Experience with physical simulation tools: OpenModelica, Simulink, Julia, Modelica, or FMI/FMU-based co-simulation.
- Hands-on projects involving physical systems — vehicle dynamics, powertrain, robotics, Baja SAE, Formula SAE, solar car, or similar.
- Familiarity with automotive SE artifacts: requirements, test cases, E/E architecture, CAN signals, DBC/ARXML.
- Experience with LLM evaluation: precision/recall, hallucination rate, latency, cost — for SE-specific use cases.
- GitLab CI/CD experience;
- AI portfolio: projects applying LLMs or ML to engineering or technical documentation — even academic or personal projects count.
- Fault tree analysis, DFEMA or STPA know-how