Sr. Vehicle Modelling Engineer, Applied AI Systems

About this role

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 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.

RESPONSIBILITIES

1. Digital Twin Development & Operation

- 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

2. AI-Assisted Systems Engineering Tooling

- 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.

3. Systems Engineering (Hands-On)

- 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

Minimum 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.

Preferred Qualifications

- 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

TOTAL REWARDS

We build the exceptional — and we believe the people doing that work should be rewarded accordingly. In addition to a competitive base salary, full-time positions may be is eligible to participate in our annual company performance bonus program.

Payments are discretionary and not guaranteed; actual amounts depend on company results and the terms of the plan in effect, and require active employment at the time of payout. This role is also eligible for equity in the form of Restricted Stock Units (RSUs), subject to board approval and the terms of our equity incentive plans, including applicable vesting requirements.

In addition to our compensation programs, we invest in our people with a comprehensive benefits package designed to support the health, wellbeing, and financial future for full-time employees — including health coverage, retirement savings, time off, and family planning programs. Offerings vary by country. Learn more about our global benefit programs…