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

About the role#

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, and scientific machine learning. You will support research aimed at improving vehicle aerodynamic performance and accelerating design exploration. This role is for a technically motivated student who enjoys applying engineering fundamentals and computational methods to complex problems. You will work alongside researchers and cross-functional partners to translate research results into practical engineering tools.

What you'll do#

• Support vehicle aerodynamics research involving external flow, drag, lift, downforce, cooling airflow, and efficiency.
• Set up, run, monitor, and post-process CFD simulations for vehicle and component-level studies.
• Prepare simulation geometry, meshes, boundary conditions, and analysis workflows.
• Analyze aerodynamic coefficients, pressure distributions, velocity fields, and wake structures.
• Build and curate datasets from CFD simulations, experimental results, and vehicle geometry.
• Develop, train, test, and evaluate machine-learning or surrogate models for aerodynamic predictions.
• Investigate physics-informed, data-driven, reduced-order, or graph-based machine learning approaches.
• Apply data preprocessing, feature engineering, model selection, and hyperparameter tuning.
• Evaluate model accuracy, robustness, and generalization across vehicle geometries.
• Compare machine-learning predictions with CFD, wind-tunnel, and experimental results.
• Support design-of-experiments, surrogate-based optimization, and sensitivity studies.
• Develop Python-based scripts and tools to automate simulation setup, data processing, and visualization.
• Document assumptions, methods, model configurations, and recommendations.
• Create technical reports, presentations, and visualizations for engineering audiences.
• Collaborate with researchers to define technical questions and review results.

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 field.
• Ability to work full-time, 40 hours per week, during the summer months.
• Coursework or project experience in fluid mechanics, vehicle aerodynamics, CFD, turbulence modeling, numerical methods, or heat transfer.
• Experience with programming languages such as Python, MATLAB, or C++.
• Experience with scientific-computing and machine-learning libraries like NumPy, pandas, SciPy, scikit-learn, PyTorch, or TensorFlow.
• Familiarity with CFD, CAE, CAD, Linux, high-performance computing, or engineering software workflows.
• Experience applying machine-learning models to scientific, engineering, or physical-simulation data.
• Familiarity with surrogate modeling, physics-informed machine learning, graph neural networks, or uncertainty quantification.
• Ability to work independently and collaborate effectively with cross-functional teams.
• Strong analytical, written, and verbal communication skills.

Location & details#

• Location: Warren, Michigan.
• Work Modality: Hybrid (expected to be on-site at least three times per week).
• Term: Summer 2027.
• Start Dates: May 24, 2027, or June 14, 2027.
• Sponsorship: General Motors does not provide immigration-related sponsorship for this role.