Research Engineer

Ford Ford · Auto · Dearborn, MI +1 · Research and Advance Engineering

Research Engineer role focused on architecting and integrating a Manufacturing Digital Thread for Ford, spanning metal forming, body construction, and paint. The role involves developing custom tools using C++ and Python, automating data flow between simulation solvers and CAD environments, and optimizing computational performance and data pipelines. It also includes leading statistical sensitivity studies, Design of Experiments (DOE), and collaborating with domain experts to create a unified digital twin for various manufacturing workflows.

What you'd actually do

  1. Research and design custom 3DEXPERIENCE (3DX)/Catia tools using C++ and Python.
  2. Architect linkage programs to automate data flow between simulation solvers (Hypermesh, LS-DYNA, AutoForm) and CAD environments.
  3. Lead statistical sensitivity studies and Design of Experiments (DOE) to optimize manufacturing robustness.
  4. Partner with Ford subject matter experts in forming, joining, and surface science to develop a unified digital twin for stamping, structural casting, and coating workflows.
  5. Streamline simulation run-times and automate data processing to enable 100% in-house feasibility validation and rapid design iterations.

Skills

Required

  • Bachelor’s degree in Mechanical Engineering, Material Science and Engineering or Computer Science and Engineering
  • 2+ years of experience in industry and graduate study
  • Architectural Logic, Data & Integration
  • Highly proficient in Python, MATLAB, or C++ for linking disparate data formats
  • experience with PLM systems such as Catia V5/V6, 3DEXPERIENCE (3DX), Siemens NX
  • Knowledge of virtual manufacturing using CAE suites such as Ansys Minerva, Abaqus, LS-DYNA, or Nastran, Simufact, ESY Sysweld

Nice to have

  • Theory: Strong foundation in Solid Mechanics (elasticity, plasticity), material deformation, and thermal-mechanical behavior.
  • Material Science: Deep understanding of stress-strain behavior, anisotropy in sheet metal, and phase transformations during welding or baking
  • Statistics: Proficiency of Monte Carlo simulations and Six Sigma principles to handle "variation" as a statistical input rather than a single deterministic value.
  • GD&T: Knowledge of Geometric Dimensioning and Tolerancing to manage dimensional "stack-ups."

What the JD emphasized

  • Generalist-Integrator
  • weaving
  • end-to-end Digital Thread
  • automated CAD/CAE applications
  • bridging engineering silos
  • architectural data integration
  • CAE-based virtual manufacturing
  • solid mechanics
  • material behavior
  • statistical variation methods
  • GD&T