AI Engineering
Learn AI Engineering to design next-generation solutions for today’s industries.
Application Deadline
Priority*: November 12, 2025
Final: December 3, 2025
Next Start Date
January 2026 (AI Engineering Fundamentals)
Program Length
9-12 months per certificate
Taught By
College of Engineering
Stay Ahead in a Fast-Moving Field
The field of engineering is experiencing a paradigm shift—companies are using artificial intelligence (AI) now more than ever before. According to a survey by Gartner, the number of organizations that heavily rely on AI increased by 270% in just 4 years.
Simply put: engineering professionals must develop AI skills to stay ahead. That’s why we offer the following online graduate certificates in AI Engineering Fundamentals and related fields:
- Graduate Certificate in AI Engineering Fundamentals
- Graduate Certificate in Advanced AI Models for Engineering
These credit-bearing, graduate-level certificates will transform you into an AI-savvy engineer who can use this state-of-the-art technology to solve real-world engineering problems in new and revolutionary ways.
Solve Engineering Problems with AI
With expertise in artificial intelligence, you can create more efficient, accurate, and productive engineering solutions that will allow your organization to compete in a rapidly-evolving landscape.
When you complete these certificates, you will be able to automate repetitive tasks, optimize designs, make predictions, improve quality control, and advance the design of products using the most cutting-edge AI and machine learning techniques.
These Elite Certificates Are Best Suited For:
- Engineers looking to stay ahead in their field and deliver value to their organizations. These programs teach fundamental skills in artificial intelligence and machine learning as they relate to solving real-world engineering problems. Students will acquire hands-on skills for developing intelligent engineering software and making an impact in their industries.
- Recent graduates with degrees in engineering and related fields seeking to expand their skills and acquire the knowledge needed for the future of engineering. When students complete these certificates, they will have highly marketable skills in AI they can directly apply to industry projects.
Learning Outcomes
In the AI Engineering Fundamentals graduate certificate, you will:
- Learn how to describe the fundamental problems addressed by machine learning and artificial intelligence.
- Design and implement various machine learning algorithms.
- Apply mathematical concepts (including linear algebra, probability, statistics, and optimization) in the context of machine learning algorithms.
- Formulate data-driven approaches to problems and communicate these solutions through implemented algorithms and write-ups.
- Learn the basics of deep neural networks and their applications to different engineering tasks.
- Describe and compare commonly used deep learning algorithms, their theoretical underpinnings, and how they are applied to solve real-world problems.
- Implement deep learning algorithms in Python using PyTorch.
- Analyze practical engineering problems to identify and apply relevant deep learning algorithms, and then communicate the approach, solutions, and outcomes.
In the Advanced AI Models for Engineering graduate certificate*, you will:
- Study advanced AI models used in engineering applications such as surrogate modeling, materials discovery, engineering design, manufacturing, and human-AI teaming.
- Build upon fundamental deep learning concepts you mastered from the first certificate. Topics will include advanced variants of convolutional neural networks, graph neural networks, generative adversarial networks, neural operators, physics-informed neural networks, and diffusion models.
- Explore Generative AI models used in engineering applications and related topics like diffusion models, transformers, and large language models.
- Learn how to solve real-world engineering problems by tailoring publicly available Generative AI systems and building upon existing software. Through course projects, you will practice designing and implementing these techniques for engineering applications.
*Please note: the Advanced AI Models for Engineering graduate certificate is still in development, so course descriptions and learning outcomes are subject to change.
# 1
in the nation
For CMU's AI graduate programs (2026 U.S. News & World Report)
# 7
in the Nation
For CMU's graduate engineering programs (2026 U.S. News & World Report)
34 members
in the National Academy of Engineering
One of the highest professional honors for engineers
Curriculum Overview
Carnegie Mellon currently offers two credit-bearing, graduate-level certificates in the field of AI Engineering. The first certificate (AI Engineering Fundamentals) is a prerequisite for the second certificate and features the following course progression.
Individuals who meet the admission requirements are encouraged to apply for the first certificate in AI Engineering Fundamentals. After mastering foundational topics in this certificate and successfully completing the coursework, these individuals may choose to explore more complex topics in the second certificate called Advanced AI Models for Engineering.
Graduate Certificate in AI Engineering Fundamentals
The online Graduate Certificate in AI Engineering Fundamentals includes two graduate-level, credit-bearing courses taught by expert CMU faculty and features the following course progression:
Course Number: 24-887
Units: 12 units
Learn fundamental artificial intelligence and machine learning techniques for developing software that is foundational to next-generation design and analysis tools. In this course, you will explore topics like supervised and unsupervised learning, feature engineering, model selection and optimization, dimensionality reduction, and ensemble learning, and then complete the course with an introduction to deep learning. You’ll not only learn the theory behind these techniques, but how to efficiently implement them as well.
Course Number: 24-888
Units: 12 units
Through hands-on activities, you will learn the foundations of deep neural networks, their applications to engineering tasks, and how to use deep learning to solve complex engineering problems. In this course, you will explore topics like convolutional neural networks, recurrent neural networks, long short-term memory, and generative adversarial networks.
Graduate Certificate in Advanced AI Models for Engineering*
The online Graduate Certificate in Advanced AI Models for Engineering includes two graduate-level, credit-bearing courses taught by expert CMU faculty. A curriculum map featuring the course progression of the certificate will be provided soon.
Each course will appear on your Carnegie Mellon transcript with the grade earned. To earn the certificate, you must successfully complete both courses in the program. However, if you are only interested in the first course, you may complete that course only and it will show on your transcript with the grade earned.
*Please note: the Advanced AI Models for Engineering graduate certificate is still in development, so course descriptions and learning outcomes are subject to change.
Course Number: 24-880
Units: 12 units
Explore advanced AI models used in engineering applications. Topics in this course build upon the core deep learning models taught in the AI Engineering Fundamentals certificate and include: advanced variants of convolutional neural networks, graph neural networks, generative adversarial networks, neural operators, physics-informed neural networks, and diffusion models. By the end of this course, you should know how to use these models in a wide range of engineering applications including surrogate modeling, materials discovery, engineering design, manufacturing, and human-AI teaming.
Coursework emphasizes the theoretical foundations and the mathematical modeling of the introduced techniques along with the implementation and testing of these techniques in software. Assignments require knowledge of Python and PyTorch at the level used in 24-888 Introduction to Deep Learning.
Course Number: 24-889
Units: 12 units
This course focuses on generative AI models used in engineering applications and covers topics like diffusion models, foundation models, transformers, and large language models. The course connects these algorithms to publicly available generative AI systems and teaches you how these systems can be tailored toward engineering applications. Each topic culminates in a mini-project where you will build upon existing software to design and implement these techniques with a focus on real-world engineering use cases.
The coursework emphasizes the theoretical foundations and the mathematical modeling of the introduced techniques. Assignments include quizzes that assess conceptual understanding as well as several guided projects that focus on software implementation, validation, and technical reporting. Assignments require knowledge of Python and PyTorch at the level used in 24-880 Advanced AI Models in Engineering.
The Building Blocks of Our Curriculum
Real-World Focused
In these programs, everything you learn serves a purpose—to help you solve real-world engineering problems. Throughout the coursework, you will practice solving problems in Jupyter Notebooks using least squares regression, support vector machines, decision trees, logistic regression, neural networks, clustering methods, dimensionality reduction techniques, ensemble learning techniques, and more. By the end, you will be able to describe and compare commonly used machine learning algorithms, explain their theoretical underpinnings, and use them to solve real-world engineering problems.
Hands-On Learning
As an engineer, you are a doer and a builder. In these programs, you will learn core concepts by implementing various machine learning algorithms from scratch (for example, in Python) and by using industry-standard packages. You will also apply mathematical foundations for machine learning, including multivariate calculus, linear algebra, statistics, and optimization. When you complete the coursework, you will feel confident formulating data-driven approaches to AI engineering problems and communicating these solutions with algorithms and write-ups.
Practical Problem Solving
The field of AI for engineering can include some “out-there ideas”—but in these programs, you’ll stay focused on what’s doable and relevant to today’s industries. Throughout the coursework, you will analyze practical engineering problems that will help you apply the machine learning concepts directly to your career, which will allow you to become more efficient, innovative, and successful in your approach and in the solutions you create.
Meet Our World-Class Faculty
Dr. Levent Burak Kara
Professor of Mechanical Engineering
Education: Ph.D., Carnegie Mellon University
Research Focus: Developing new computational analysis, design, and manufacturing technologies with wide-ranging applications in areas like mechanical CAD, topology optimization, additive manufacturing, electronics design, and bio-engineering. In his research, Dr. Kara combines principles of machine learning, optimization, and geometric modeling to develop new knowledge and computational software for use in next-generation design systems.
Research Lab: Visual Design and Engineering Lab
Dr. Amir Barati Farimani
Associate Professor of Mechanical Engineering
Education: Ph.D., University of Illinois at Urbana-Champaign
Research Focus: Applying machine learning, data science, and molecular dynamics simulations to health and bio-engineering problems. Dr. Farimani’s lab unites researchers with different backgrounds (including physics, materials science, mechanical engineering, bio-engineering, chemical engineering, and computer science) to bring the state-of-the-art machine learning algorithm to mechanical engineering.
Research Lab: Mechanical and AI Lab (MAIL)
Is this the right program for you?
Let’s face it, pursuing any kind of advanced training is an investment of your time, energy and resources. Before you begin your application for the AI Engineering Fundamentals certificate, take a moment to review the program requirements below.
Successful applicants will have:
A bachelor’s degree in engineering or a related field: While geared toward students with a background in engineering, other degrees with a significant quantitative focus in math, physics, or chemistry will also be considered.
Proficiency in mathematical concepts: Applicants should have successfully completed coursework and have proficiency in basic linear algebra, calculus, statistics, and probability theory.
A motivated mindset and an innate curiosity: Harder to measure, but equally important, successful applicants will have an inquisitive mind, a hunger to learn, and a willingness to ask questions when the material gets tough. With two credit-bearing, graduate-level CMU courses, a consistent and conscious effort will be required to master each topic.
Experience with Python or other programming languages: Experience with simple file inputs/outputs, arrays and matrices, functions, class and for/while loops is important, as is a familiarity with NumPy and SciPy. Experience with other scientific programming languages like Matlab, Octave, R, C/C++, or Java is also helpful.
Students with limited experience in programming are encouraged to take CMU's free online course, Principles of Computation with Python.
Application Checklist
Ready to Apply?
Here’s what you’ll need to complete the application process for the Graduate Certificate in AI Engineering Fundamentals:
Complete the online application
Submit your application online in less than 30 minutes.
*If you have successfully completed the first certificate in AI Engineering Fundamentals and would like to start the second certificate, you do not need to reapply. Please contact your Program Specialist for next steps.
Submit your resume/CV
We’d like to learn more about your employment history, academic background, technical skills, and professional achievements. Submit a 1 to 2 page resume or CV showcasing your experience.
Submit your transcripts
Submit an unofficial copy of your transcript for each school you attended. Transcripts must include your name, the name of the college or university, the degree awarded (along with the conferral date), as well as the grade earned for each course. Email your transcripts directly to apply@online.cmu.edu. Please note: former Carnegie Mellon students and/or alumni can request a copy of their CMU transcript from The Hub.
Upload a statement of purpose
Tell us your professional story. Where have you been, and where do you hope to go? In 500 words or less, please share how our program would advance your capabilities in your current role or prepare you for a new role in the industry.
Submit your TOEFL, IELTS, or DuoLingo test scores (if applicable)
An official TOEFL, IELTS, or Duolingo test is required for non-native English speakers. Please note, Carnegie Mellon University has minimum requirements for all language test scores.
However, this requirement may be waived for applicants who meet one of the following:
- Completed an in-residence bachelor’s, master’s, or doctoral degree program in the United Kingdom, United States, or Canada (excluding Quebec)
- Have at least three years of professional work experience where English was the primary language of communication. In this case, you will be prompted to describe your work experience in your application and include a link to your company’s website for verification.
So, What Is the Investment Per Course?
Below is a tuition breakdown for the AI Engineering Fundamentals and the Advanced AI Models for Engineering certificates during the 2025/2026 academic year:
Graduate Certificate in AI Engineering Fundamentals
| Semester | Course | Units | Investment |
|---|---|---|---|
| Fall 2025 | Machine Learning & Artificial Intelligence for Engineers | 12 units | $8,484 |
| Spring 2026 | Introduction to Deep Learning | 12 units | $8,484 |
| Total Investment | $16,968 | ||
Graduate Certificate in Advanced AI Models for Engineering
| Semester | Course | Units | Investment |
|---|---|---|---|
| A | Advanced AI Models for Engineering | 12 units | $8,484 |
| B | Generative AI with Applications | 12 units | $8,484 |
| Total Investment | $16,968 | ||
Additional Fees & Notes
- A technology fee of approximately $245 will be assessed each semester (subject to change).
- The rates above are for the 2025/2026 academic year only. If the certificates are not completed within that time frame, tuition may increase slightly for the following academic year.
Fellowship Opportunities
All students who submit their application by the priority deadline will receive a partial fellowship to help offset the cost of tuition. If admitted to the program, the amount of your award will be communicated with your admissions letter.
Students who submit after the priority deadline may be eligible for any remaining fellowships that are still available. But we highly encourage you to apply by the priority deadline if you are interested in a fellowship award.
In addition, Carnegie Mellon alumni are eligible for a fellowship to the Graduate Certificate in AI Engineering Fundamentals worth up to 20% of tuition. Indicate your alumni status within the application to be eligible.
About the Department of Mechanical Engineering
The Graduate Certificate in AI Engineering Fundamentals and the Graduate Certificate in Advanced AI Models for Engineering are offered by the Department of Mechanical Engineering (MechE), which is housed within CMU’s highly-ranked College of Engineering. MechE faculty members are highly distinguished in their field and many of them are currently collaborating on high-profile projects with AI and machine learning technology. Some of their work includes:
- Applying ML techniques to automate the design process of electronic circuits and chips
- Using AI to design a better method for desalination and provide the world with drinking water
- Comparing the effectiveness of human versus human-AI teams in the context of engineering
- Using deep learning to research material transport in the brain, which could improve our understanding of disease development
About the College of Engineering
Carnegie Mellon’s College of Engineering faculty work is at the forefront of research and innovation, applying deep expertise to the world’s most pressing challenges. The College of Engineering leads work in a diverse and expanding array of fields, from autonomous systems and clean energy to bioengineering and advanced manufacturing.
Engineering faculty design learning experiences with intention. Courses are grounded in real-world applications, shaped by research on how people learn best, and focused on giving students the tools to lead in fast-changing industries.