Machine Learning Research Internships
Machine learning research internships give university students, recent graduates, and early-career switchers hands-on project experience, mentorship from working research scientists and engineers, and, at many employers, a path toward a full-time offer. Openings cover Investment & Asset Management, with Meta, Microsoft, and General Motors (GM) among the employers posting roles now.
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Job Description
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, 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.
GM’s related 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 Team:
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 to identify and develop technologies with potential value to GM.
You will work in a multidisciplinary environment that 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 experience may also include innovation challenges, interactions with industry leaders, cross-functional networking events, technical presentations, and community-service activities.
What You’ll 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.
Required Qualifications:
- 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.
Preferred Qualifications:
- 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.
Why Join Us
We believe we all must make a choice every day – individually and collectively – to drive meaningful change through our words, our deeds and our culture. Every day, we want every employee to feel they belong to one General Motors team.
Benefits Overview
From day one, we're looking out for your well-being–at work and at home–so you can focus on realizing your ambitions. Learn how GM supports a rewarding career that rewards you personally by visiting Total Rewards resources .
Non-Discrimination and Equal Employment Opportunities (U.S.)
General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers.
All employment decisions are made on a non-discriminatory basis without regard to sex, race, color, national origin, citizenship status, religion, age, disability, pregnancy or maternity status, sexual orientation, gender identity, status as a veteran or protected veteran, or any other similarly protected status in accordance with federal, state and local laws.
We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role-related assessment(s) and/or a pre-employment screening prior to beginning employment. To learn more, visit How we Hire .
Accommodations
General Motors offers opportunities to all job seekers including individuals with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, email us or call us at 1-800-865-7580. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.
Job Description
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, 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.
GM’s related 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 Team:
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 to identify and develop technologies with potential value to GM.
You will work in a multidisciplinary environment that 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 experience may also include innovation challenges, interactions with industry leaders, cross-functional networking events, technical presentations, and community-service activities.
What You’ll 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.
Required Qualifications:
- 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.
Preferred Qualifications:
- 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.
Why Join Us
We believe we all must make a choice every day – individually and collectively – to drive meaningful change through our words, our deeds and our culture. Every day, we want every employee to feel they belong to one General Motors team.
Benefits Overview
From day one, we're looking out for your well-being–at work and at home–so you can focus on realizing your ambitions. Learn how GM supports a rewarding career that rewards you personally by visiting Total Rewards resources .
Non-Discrimination and Equal Employment Opportunities (U.S.)
General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers.
All employment decisions are made on a non-discriminatory basis without regard to sex, race, color, national origin, citizenship status, religion, age, disability, pregnancy or maternity status, sexual orientation, gender identity, status as a veteran or protected veteran, or any other similarly protected status in accordance with federal, state and local laws.
We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role-related assessment(s) and/or a pre-employment screening prior to beginning employment. To learn more, visit How we Hire .
Accommodations
General Motors offers opportunities to all job seekers including individuals with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, email us or call us at 1-800-865-7580. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.
Machine Learning Research Internship Market
Who's Hiring
- Meta13
- Microsoft4
- General Motors (GM)2
- Rivian1
- NEC Labs1N
Top Industries Hiring
- Investment & Asset Management
Tips for Your Machine Learning Research Internship Search
Apply in the fall for summer research roles
Large research labs and tech companies open summer intern applications as early as August or September. If you wait until spring, the most structured cohort programs are already closed. Set a calendar reminder to start searching in early fall and submit priority applications before November.
Build a project portfolio recruiters can read
Without a long work history, your projects are your application. Document two or three end-to-end ML experiments, note the research question, the dataset, the model choices, and the results, and publish the code and write-up in a public repository. A reviewable artifact is what separates your application from a list of course names.
Work your campus network and apply directly at the same time
Campus career fairs surface structured programs tied directly to your university, and professors or career center staff often know which labs recruit from your school before roles post publicly. Applying directly to companies running smaller research cohorts alongside campus activity reaches employers who never attend university events.
Practice your technical screen out loud before you apply
ML research intern interviews typically include a coding screen on data structures and algorithms alongside a research discussion where you walk through a past project or paper. Practice explaining your reasoning aloud, not just arriving at the right answer, interviewers are evaluating how you think through ambiguous problems as much as whether your code compiles.
Target structured research intern programs early
Several large technology and industrial research organizations run dedicated cohort programs designed to train people new to ML research, recruiting months before the internship start date. Identify the programs that match your research interests and apply in the first wave, these fill faster than general internship postings at the same companies.
Set your work-type filter before you start
On-site roles are 67% of the machine learning research internships listed here. Decide what you can actually commit to, on-site, hybrid, or remote, before you start reviewing listings on Migrate Mate, and filter by both work type and location so you spend time only on roles you can take.
Machine Learning Research Internships: Frequently Asked Questions
How do I get a machine learning research internship?
Lead with coursework and personal projects rather than work history, hiring teams expect limited professional experience at the intern level. A linked GitHub repository with documented ML experiments or published research notebooks gives recruiters something concrete to assess. Combine direct applications with campus career fairs, where ML research recruiters often move faster for students they meet in person.
Can a machine learning research internship turn into a full-time job?
Many employers extend return offers to strong interns, but conversion is never guaranteed. For machine learning research interns, what drives it most is delivering real results on an assigned project, building relationships with the team, and whether the org has open headcount when your internship ends. Treat it as a genuine audition without counting on an offer at the start.
When should I apply for machine learning research internships?
Earlier than most students expect. Large tech companies and research labs recruit summer interns the preceding fall, often closing applications by October or November. Smaller companies and co-op programs post closer to actual start dates, so openings appear year-round. Checking listings regularly rather than waiting for a single application season gives you the widest window.
Are machine learning research internships paid?
Most professional machine learning research internships in the U.S. are paid. Compensation varies by company size, industry, and location. Where an employer discloses pay, it appears directly in the listing, so filtering listings gives you a practical sense of what the market looks like without relying on aggregate estimates.
What should a machine learning research internship resume include?
Lead with projects, not work history. Include two or three complete, documented projects that name the methods, frameworks, and datasets used, and link directly to the code repository or published notebook so reviewers can assess your work. Add relevant coursework in machine learning, statistics, or algorithms. Keep the entire resume to one page.
Are there remote machine learning research internships?
Yes. Remote and hybrid roles make up 33% of the machine learning research internship listings here, with the rest on-site. Remote cohorts fill quickly once posted, so apply as soon as you find a fit. Use the work-type filter to surface them without sorting through every listing.
What is an ML research internship vs. a software engineering internship?
An ML research internship centers on designing and running experiments, implementing novel models, and contributing to research output such as papers or internal technical reports. A software engineering internship focuses on building and shipping product features. ML research roles expect familiarity with academic literature and experimental methodology, not just coding ability, and are typically hosted within a dedicated research org or lab team.
Can international students get machine learning research internships?
Yes. F-1 students can intern through CPT while enrolled or through OPT work authorization after finishing a degree, and the employer does not have to file anything for either, so many companies are open to international interns. Confirm your eligibility and timing with your university's international student office before accepting an offer.
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