EV Powertrain: Applied AI Engineer

Applied AI Engineer – EV Powertrain Location: Lausanne, Switzerland or Cambridge, UK

About the role Neural Concept's Vertical Solutions team builds data-driven workflows for specific engineering applications that sit on top of the Neural Concept platform.

Working alongside the Solution Owner — who defines product direction and customer value — you bring the deep engineering domain expertise and hands-on technical build capability needed to turn that direction into a working solution.

You combine real-world EV Powertrain design experience with fluency in modern AI tooling, including foundation models and LLM-based build assistants, to prototype and ship production-grade workflows quickly.

Build data-driven engineering workflows for EV Powertrain applications (electric motor, gearbox, battery, EDU) on the Neural Concept platform, translating the Solution Owner's product direction into working solutions.

Work with lead customers to understand and solve their challenges

Apply deep domain knowledge to ensure workflows reflect real engineering practice, constraints, and edge cases that a non-domain builder would miss.

Hands-on prototyping using AI/ML tooling — foundation models, LLM-based coding assistants (e.g.

Claude), and Python/CAE scripting — to build and iterate rapidly.

Work day-to-day with CAD/CAE/ML engineers to take prototypes from first pass to production-ready quality.

Validate outputs against real-world engineering standards, using your domain judgment as the quality bar.

Feed technical and domain insight back into the broader roadmap based on what you learn while building.

Who you are

5+ years of hands-on engineering experience at an OEM or Tier 1 supplier, designing electric motors, gearboxes, batteries, or other EDU sub-systems — system-level experience preferred.

Fluent in Powertrain simulation tools (Motor-CAD, Maxwell, JMAG, Romax) and/or system-level tools (MATLAB/Simulink, Amesim, or in-house tools).

Proficient, hands-on user of modern AI tooling — comfortable building real engineering solutions with foundation models and LLM-based tools such as Claude or Codex, not just casually experimenting with them.