Sr. Vehicle Modelling Engineer, Applied AI Systems

Rivian and Volkswagen Group Technologies•Irvine, CA

About The Position

The Systems Design Reliability Engineering (SDRE) team is building the next generation of AI-assisted, model-driven systems engineering at Rivian VW Group. This role involves replacing traditional requirements processes with simulation-first design, Digital Twin-based verification, and test coverage. The engineer will work at the intersection of AI tooling and physical system modelling, developing and operating the AI tooling and Digital Twin infrastructure for Vehicle Controls, Infotainment, Communications, and Access. The role includes end-to-end ownership of features, from building plant models and co-simulation environments to deploying LLM-assisted requirement and test pipelines. It also involves hands-on systems engineering tasks such as authoring requirements, performing analyses, participating in design reviews, STPA, and applying SDRE methods.

Requirements

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.

Nice To Haves

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.

Responsibilities

Build and maintain multi-fidelity plant models (FMU-packaged) for vehicle subsystems (powertrain, dynamics, thermal, body) using Python, OpenModelica, Julia, or Simulink.
Develop and run co-simulation environments (FMI-based) that pair vECUs with plant models for model-to-code simulation, SIL regression tests, and field issue replay.
Correlate models against real vehicle, dyno, lab rig, and fleet data.
Build LLM pipelines for requirement drafting, test script generation, coverage gap analysis, and root cause analysis over SE artifacts.
Deploy semantic search and RAG over requirements, architecture models, and test scripts using modern LLM app stacks (LangChain, LlamaIndex, or equivalent).
Integrate AI assistants into GitLab and test management systems via APIs, plugins, and CI/CD pipelines.
Build AI analytics tools that correlate requirements, architecture changes, and calibrations with fleet data, field issues, and test failures.
Author requirements and test cases as a practicing systems engineer, applying RequiTest and test-driven SE methods.
Participate in architecture, interface, and safety design reviews across domains.
Document AI-augmented SE process standards and playbooks; help drive adoption across programmes.
Capture process patterns from domain teams and convert them into AI-supported workflows, with human-in-the-loop guardrails.