2027 Summer Intern – Research and Development – Vehicle Aerodynamics and Machine Learning

The role

Job Description

As a 2027 Summer Intern on the Vehicle Aerodynamics and Machine Learning team, you will work at the intersection of physics-based vehicle simulation, computational fluid dynamics, scientific machine learning, and aerodynamic design optimization.

You will support research that uses CFD and AI/ML to improve vehicle aerodynamic performance, accelerate design exploration, and enable more efficient development of next-generation vehicles. The work may include analyzing vehicle flow fields, developing data-driven aerodynamic models, evaluating design concepts, and translating research results into practical engineering tools.

This role is intended for an inquisitive, technically motivated student who enjoys applying engineering fundamentals and modern computational methods to complex problems. You will work with experienced researchers and cross-functional engineering partners while developing your skills in vehicle aerodynamics, simulation, machine learning, data analysis, and technical communication.

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2027 Summer Intern – Research and Development – Vehicle Aerodynamics and Machine Learning

General Motors, Warren, Michigan

The role

Job Description

What you will do

Support vehicle aerodynamics research involving external flow, aerodynamic drag, lift, downforce, cooling airflow, and vehicle efficiency.
Set up, run, monitor, and post-process CFD simulations for vehicle and component-level aerodynamic studies.
Prepare simulation geometry, meshes, boundary conditions, design variables, and analysis workflows.
Analyze aerodynamic coefficients, pressure distributions, velocity fields, wake structures, flow features, and other performance metrics.
Build and curate datasets from CFD simulations, experimental results, vehicle geometry, and design variables.
Develop, train, test, and evaluate machine-learning or surrogate models for aerodynamic quantities and flow-field predictions.
Investigate physics-informed, data-driven, reduced-order, graph-based, or other scientific machine-learning approaches relevant to vehicle aerodynamics.
Apply data preprocessing, feature engineering, dimensionality reduction, model selection, hyperparameter tuning, and validation techniques.
Evaluate model accuracy, robustness, generalization, uncertainty, and limitations across vehicle geometries and operating conditions.
Compare ML predictions with CFD, wind-tunnel, experimental, or other trusted engineering results.
Support design-of-experiments, surrogate-based optimization, sensitivity studies, and aerodynamic shape exploration.
Develop Python-based scripts, tools, and workflows to automate simulation setup, data processing, model training, visualization, and reporting.
Help identify opportunities to reduce analysis time and enable earlier aerodynamic feedback during vehicle design.
Document assumptions, methods, data sources, model configurations, results, limitations, and recommendations.
Create clear plots, visualizations, technical reports, presentations, and demonstrations for engineering and research audiences.
Collaborate with researchers and cross-functional partners to define technical questions, review results, and recommend next steps.
Present technical findings and participate in innovation, networking, and professional-development activities.

Nice to have

Coursework, research, or project experience in fluid mechanics, vehicle aerodynamics, computational fluid dynamics, turbulence modeling, numerical methods, optimization, or heat transfer.
Experience with Python, MATLAB, C++, or comparable programming languages.
Experience with NumPy, pandas, SciPy, scikit-learn, PyTorch, TensorFlow, or similar scientific-computing and machine-learning libraries.
Familiarity with CFD, CAE, CAD, meshing, visualization, Linux, high-performance computing, or engineering software workflows.
Experience developing or applying machine-learning models to scientific, engineering, physical-simulation, or time-series data.
Familiarity with surrogate modeling, physics-informed machine learning, graph neural networks, neural operators, transfer learning, uncertainty quantification, or model explainability.
Experience with design-of-experiments, parameter studies, optimization, or automated engineering workflows.
Ability to evaluate data quality, identify modeling limitations, and distinguish physically meaningful results from numerical or data artifacts.
Experience communicating technical results through reports, presentations, plots, code documentation, or demonstrations.
Strong analytical, written, and verbal communication skills.
Ability to work independently while collaborating effectively with researchers and cross-functional engineering teams.

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

This role is categorized as hybrid. This means the successful candidate is expected to report to Warren, MI three times per week, at minimum [or other frequency dictated by the business]

Start dates for this internship role are May 24th & June 14th of 2027. 



 About GM

Our vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all.

2027 Summer Intern – Research and Development – Vehicle Aerodynamics and Machine Learning

The role

2027 Summer Intern – Research and Development – Vehicle Aerodynamics and Machine Learning

The role

The team

What you will do

What you need

Nice to have

Work Arrangement

Non-Discrimination and Equal Employment Opportunities (U.S.)

Pay and benefits

About General Motors

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