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

General Motors•Warren, MI

11d•Hybrid

About The Position

As a 2027 Summer Intern on GM’s Vehicle Aerodynamics and Machine Learning team, you will work at the intersection of physics-based vehicle simulation, computational fluid dynamics (CFD), scientific machine learning, and aerodynamic design optimization. You will support research using CFD and AI/ML to improve vehicle aerodynamic performance, accelerate design exploration, and enable more efficient development of next-generation vehicles. Work may include analyzing vehicle flow fields, developing data-driven aerodynamic models, evaluating design concepts, and translating research results into practical engineering tools. The 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 skills in vehicle aerodynamics, simulation, machine learning, data analysis, and technical communication. Related GM research includes high-fidelity CFD, aerodynamic drag and efficiency improvement, physics-based AI models, surrogate modeling, transfer learning, and rapid aerodynamic feedback from vehicle geometry or design data. The Energy and Propulsion Systems Research team conducts applied research intended to improve vehicle efficiency, performance, range, cost, and sustainability. The team collaborates with researchers, vehicle-development organizations, engineering groups, universities, technology partners, and industry leaders. The multidisciplinary environment may include vehicle aerodynamicists, CFD specialists, data scientists, machine-learning researchers, vehicle designers, optimization experts, and engineers from propulsion, thermal, body, chassis, and vehicle-performance organizations. The internship may also include innovation challenges, interactions with industry leaders, cross-functional networking events, technical presentations, and community-service activities.

Requirements

Pursuing 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 full time, 40 hours per week during the summer months.
Do not apply if you will need GM immigration sponsorship now or in the future, including company sponsorship, GM as immigration employer of record on a government form, or work authorization requiring written submission or other immigration support.

Nice To Haves

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.

Responsibilities

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 machine-learning 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.