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Summer Intern – Motorsports Aerodynamics

Job in Concord, Cabarrus County, North Carolina, 28027, USA
Listing for: Paddock
Full Time, Seasonal/Temporary, Apprenticeship/Internship position
Listed on 2026-10-08
Job specializations:
  • Engineering
    Mechanical Engineer
Salary/Wage Range or Industry Benchmark: 28000 - 41000 USD Yearly USD 28000.00 41000.00 YEAR
Job Description & How to Apply Below
Position: 2027 Summer Intern – Motorsports Aerodynamics
# 2027 Summer Intern – Motorsports Aerodynamics General Motors Concord, North Carolina, United States Posted 27 Sept 2026## About this role

Job Description The TeamGM Motorsports is a high-performance engineering organization competing at the highest levels of global motor sport. 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 Summer Intern, you will contribute to aerodynamic development within GM Motorsports. Depending on team priorities and your background, your work may involve computational fluid dynamics (CFD), physical testing, automation, data processing, machine learning, or artificial intelligence applications for engineering.

What You’ll Do:

Example assignments may include:

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.

Projects will be defined by team needs and aligned with the candidate’s experience, interests, and development goals.

Required Qualifications:

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.

Strong analytical and quantitative problem-solving skills grounded in physical principles.

Demonstrated engineering capability through research, coursework, or prior technical projects.

Ability to communicate complex technical findings clearly to both specialized and cross-functional audiences.

Curiosity, initiative, and a commitment to advancing engineering methods and technology.

Preferred Qualifications:

Strong interest in motorsports and high-performance vehicle development.

Experience architecting, training, debugging, or evaluating deep-learning models.

Experience using CFD to develop or optimize aerodynamic designs.

Experience with scientific programming, software development, version control, CI/CD, or Dev Ops practices.

Experience with CAD, geometry processing, meshing, high-performance computing, or engineering data pipelines.

Experience working with experimental data,…
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