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

General Motors

. Support vehicle aerodynamics research involving external flow, aerodynamic drag, lift, downforce, cooling airflow, and vehicle efficiency .

Posted 10/30/2026internshipWarren • Michigan • United StatesEntry LevelWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in vehicle aerodynamics research, including CFD simulations, machine learning model development, and data analysis. Proficient in Python and relevant scientific computing libraries to automate workflows and enhance aerodynamic performance evaluation.

Highest-signal resume keywords

CFD SimulationsMachine Learning Model DevelopmentPython ProgrammingData AnalysisVehicle Aerodynamics

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Computational Fluid DynamicsFluid MechanicsTurbulence ModelingNumerical MethodsOptimizationHeat TransferSurrogate ModelingData PreprocessingDimensionality ReductionModel Evaluation

Soft Skills

Analytical SkillsWritten CommunicationVerbal CommunicationCollaborationIndependence

Tools & Technologies

PythonMATLABC++NumPyPandasSciPyScikit-learnPyTorchTensorFlowLinux

Industry Keywords

Vehicle EfficiencyAerodynamic DragLiftDownforceCooling AirflowDesign-of-ExperimentsParameter StudiesHigh-Performance ComputingEngineering Software WorkflowsTechnical Reporting

Tech Stack

Tools & technologies

AirflowLinuxNumpyPandasPythonPyTorchScikit-LearnTensorflowC++

About the role

Key responsibilities & impact

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 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

Requirements

What you’ll need

Pursuit of an advanced degree (MS or PhD) in mechanical engineering, aerospace engineering, computational engineering, computer science, applied mathematics, physics, data science, or a related technical discipline
Able to work fulltime, 40 hours per week during the summer months
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
Must not require GM immigration sponsorship now or in the future; GM does not provide immigration-related sponsorship for this role

Benefits

Comp & perks

Benefits overview and Total Rewards resources supporting employee well-being at work and home
Innovation challenges
Cross-functional networking events
Technical presentations
Community-service activities
Professional-development activities