The board
- Hours
- Full time
- Where
- Concord, North Carolina, United States of America
- Posted
- Sep 28
Job Description
The Team
GM Motorsports is a high-performance engineering organization competing at the highest levels of global motorsport. Our programs span Cadillac Formula 1, Chevrolet NASCAR and INDYCAR, and Cadillac sports-car racing through IMSA and the LMDh program. Across these programs, our engineers develop and apply expertise in aerodynamics, vehicle performance, simulation, wind-tunnel testing, data science, software, tires, propulsion, and vehicle systems.
Motorsports is a technology test bed for GM. The methods, tools, and insights developed at the racetrack can accelerate production-vehicle engineering, while GM’s production engineering capabilities help advance race-car performance. As an intern, you will work alongside experienced engineers and technical leaders on projects that connect high-performance racing with real-world engineering innovation.
The Role:
As a…
What you'd do
- Develop and improve CFD workflows across CAD preparation, geometry cleanup, meshing, solver execution, post-processing, and automated reporting.
- Develop tools and methods for wind-tunnel aerodynamic testing, including test planning, instrumentation, data acquisition, controls, and data processing.
- Develop and validate machine-learning or surrogate models that improve aerodynamic performance prediction and flow-field inference.
- Develop AI-enabled tools that improve engineering productivity, accelerate analysis, and support technical decision-making.
- Work with engineers and technical leaders to define requirements, evaluate results, document methods, and communicate recommendations.
- Pursuit of a bachelor's degree in one of the following areas: physics, mathematics, aerospace engineering, mechanical engineering, computational science, or a closely related field.
- Must be graduating after September 2027 or beyond
- Able to work fulltime, 40 hours per week during the summer months
- Demonstrated experience developing CFD surrogate models, reduced-order models, or other data-driven methods for performance prediction or flow-field inference.
- A strong foundation in one or more of the following areas: mechanical design, aerodynamics, CFD, computer vision, three-dimensional generative design, machine learning, or scientific computing.
- Starts
- 2026-09-28