2027 Summer Intern – Sportscar Motorsports Strategy

Descripción

The Role:

GM is seeking a Sportscar Motorsports Strategy intern to perform complex analytical work supporting the GM Motorsports program. The intern will support race-strategy initiatives by applying vehicle dynamics, race engineering, and motorsports strategy principles, along with programming knowledge, to existing and future simulation tools.

The Team:

The Sportscar Motorsports Strategy team supports race-strategy and vehicle-performance decision-making across GM’s IMSA and FIA World Endurance Championship (WEC) programs. The team analyzes vehicle, tire, weather, traffic, race, and simulation data; develops and evaluates strategy models; improves simulation workflows; and works with engineers and strategists to investigate performance trends, evaluate alternatives, quantify uncertainty, and communicate technical findings.

What You’ll Do:

• Analyze race, vehicle, tire, weather, and simulation data to identify trends and performance opportunities.

• Support the development, testing, and improvement of race-strategy and vehicle-performance simulation tools.

• Apply engineering fundamentals, statistics, optimization, and dynamic-systems concepts to practical motorsports problems.

• Develop scripts, visualizations, and analytical workflows that improve the speed, consistency, and usability of engineering decisions.

• Explore appropriate applications of artificial intelligence and machine learning, including performance prediction, time-series analysis, classification, anomaly detection, parameter estimation, and surrogate modeling.

• Validate analytical and machine-learning results against engineering expectations, simulation outputs, and observed race data.

• Document methods, assumptions, limitations, and results, and communicate recommendations to technical and cross-functional audiences.

Required Qualifications:

• Pursuing a Bachelor’s degree in Mechanical Engineering, Aerospace Engineering, Electrical Engineering, Mathematics, Physics, Data Science, or a related field.

• Expected graduation between December 2027 to June 2029.

• Able to work full-time, 40 hours per week, during the summer months.

• Familiarity with Python, AI/ML concepts, and/or .NET programming technologies.

• Ability to organize and analyze data, explain technical results, and work effectively in a collaborative engineering environment.

Preferred Qualifications:

• Coursework, project, research, or internship experience involving vehicle dynamics, motorsports, controls, simulation, optimization, statistics, or data science.

• Experience using Python libraries.

• Familiarity with time-series data, regression, classification, clustering, anomaly detection, or probabilistic modeling.

• Understanding of model explainability, uncertainty, data quality, and responsible use of generative AI tools in engineering work.

• Ability to work with large datasets and communicate results through clear visualizations and concise technical documentation.

GM does not provide immigration-related sponsorship for this role. Do not apply for this role if you will need GM immigration sponsorship now or in the future. This includes direct company sponsorship, entry of GM as the immigration employer of record on a government form, and any work authorization requiring a written submission or other immigration support from the company (e.g., H1-B, OPT, STEM OPT, CPT, TN, J-1, etc.)

Work Arrangement:

Onsite: This role is categorized as onsite. This means the successful candidate is expected to report to Concord, North Carolina, on a full-time basis.

Additional Posting Details:

Benefits: Paid U.S. GM holidays, GM Family First Vehicle Discount Program, and potential for growth within GM.

GM will provide a one-time lump sum taxable stipend payment to eligible students selected for the 2027 Student Program. To help facilitate administration of relocation benefits, please apply using the permanent address you would move from.

Start dates for this internship role are May 24, 2027 or June 14, 2027.

Información sobre diversidad

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Adecuaciones (EE.UU. y Canadá)

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