Powertrain Controls Simulation Model Developer - Plant Modeling

Ford Ford · Auto · Dearborn, MI +1 · Ford Next Businesses

This role focuses on developing high-fidelity plant models (engines, electrical networks, transmissions) for automotive powertrain controls simulation. The goal is to create virtual environments for testing and validating control software using Model-in-the-Loop (MIL), Software-in-the-Loop (SIL), and Hardware-in-the-Loop (HIL) testing, thereby reducing the need for physical prototypes. The role involves architecting and designing models in Simulink/Simscape, leading projects, developing parameterization strategies, integrating with simulation infrastructure, and validating models using CI pipelines. It's a hybrid role combining physical systems modeling with software engineering best practices.

What you'd actually do

  1. Architect & Design Plant Models
  2. Lead Whitespace Projects
  3. Develop Parameterization Strategies
  4. Integrate with Simulation Infrastructure
  5. Validate & Automate

Skills

Required

  • B.S. in Mechanical Engineering, Electrical Engineering, Systems Engineering, or a related technical field.
  • 3+ years of experience designing, building, and validating simulation models of automotive powertrain or vehicle systems.
  • 3+ years of hands-on experience using MATLAB, Simulink, and Simscape.

Nice to have

  • M.S. in Mechanical Engineering, Electrical Engineering, or a related technical field.
  • Direct experience testing and validating ECU control strategies in Model-in-the-Loop (MIL), Software-in-the-Loop (SIL), and Hardware-in-the-Loop (HIL) environments.
  • Experience with scripting languages (Python, MATLAB m-scripting) to automate simulation execution and data analysis.
  • Familiarity with Agile workflows and project management tools like Jira.
  • Experience with code-base version control systems like Git.
  • Experience developing models within an established software ecosystem, adhering to strict interface standards (e.g., using S-functions, reference models, variant subsystems, or Functional Mock-up Units/FMUs).
  • Experience leveraging AI-assisted engineering tools (such as GitHub Copilot and Model Context Protocol/MCP extensions) to accelerate model development, debugging, and testing.

What the JD emphasized

  • eliminate the dependency on physical prototypes
  • build Ford's "digital twins" to eliminate dependency on physical prototypes
  • AI-assisted engineering tools