Sr. Vehicle Modeling Engineer, Applied AI Systems

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.

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 three core use cases:

  • Model-to-code — simulate vehicle behaviors against a plant to develop and validate control requirements before a line of production code is written.
  • SIL regression tests — run SIL/virtual ECU controllers against plant models in nightly CI to catch regressions early and expand corner-case coverage.
  • Field issue replay — reproduce field failures in the digital twin, verify fixes virtually before shipping.

• Correlate models against real vehicle, dyno, and 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, 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 — surfacing similar historical problems and candidate fault paths.

• 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.

Qualifications

• 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.