Master’s in Artificial Intelligence Engineering-Chemical Engineering

2 Years On Campus Masters Program

Carnegie Mellon University

Program Overview

Carnegie Mellon University’s M.S. in Artificial Intelligence Engineering–Chemical Engineering (MS in AIE-ChE) combines advanced artificial intelligence and machine learning with chemical engineering, preparing students to apply AI to complex engineering and industrial challenges. The three-semester program suits students with a background in chemical engineering or a related discipline who want to develop expertise in machine learning, deep learning, process systems modeling, simulation, and AI-driven engineering solutions.

Curriculum Structure:

Three-Semester Program: Students begin with four AIE core courses totaling 42 units, including 06-763 Systems and Toolchains for AI Engineers, 24-787 Machine Learning & AI for Engineers, and 24-788 Introduction to Deep Learning, developing the computational and AI foundations needed for engineering applications. They also complete at least three Chemical Engineering core courses, selecting from subjects such as 06-623 Mathematical Modeling of Chemical Engineering Processes, 06-625 Chemical & Reactive Systems, 06-663 Analysis & Modeling of Transport Phenomena, and 06-665 Process Systems Modeling.

Students then use 36 elective units, including 12 units of project work, to personalize their degree around areas such as advanced modeling, optimization and data science, biopharmaceutical engineering, air quality and climate, or decarbonization and energy transition. Elective examples include 06-720 Advanced Process Systems Engineering, 06-681 Data Science and Machine Learning in Chemical Engineering, 06-722 Bioprocess Design, and 24-722 Energy Systems Modeling, while professional development is supported through 06-608 Graduate Professional Development Seminar and 39-699 Career & Professional Development for Engineering Master's Students.

Focus Areas:

Advanced modeling, optimization and data science, artificial intelligence and machine learning, deep learning, process systems modeling, chemical and reactive systems, transport phenomena, biopharmaceutical engineering, air quality and climate, decarbonization, sustainability and energy transition technologies, soft matter, colloids and complex fluids.

Learning Outcomes:

Students develop skills in modern machine-learning algorithms, AI systems and toolchains, deep neural networks, trustworthy AI, numerical methods, process simulation, physical and chemical process modeling, process systems engineering, and the application of AI to challenging chemical engineering problems. The program also develops Python-based data-analysis capabilities and interdisciplinary skills for integrating AI with chemical engineering processes.

Professional Alignment (Accreditation):

The MS in AIE-ChE is a graduate program and is not separately identified as an ABET-accredited degree. CMU’s Chemical Engineering department states that its undergraduate Chemical Engineering program is accredited by the Engineering Accreditation Commission of ABET under the criteria for Chemical, Biochemical, Biomolecular and similarly named engineering programs.

Reputation (Employability / Rankings):

Carnegie Mellon’s College of Engineering is ranked #10 nationally for graduate engineering in the 2027 U.S. News & World Report rankings, with Chemical Engineering ranked #14 among graduate programs. CMU also reports that its College of Engineering has strong technology and career outcomes, including a #10 ranking among private colleges for tech salaries in the Wall Street Journal’s 2024 ranking.

The program also provides documented career outcomes in roles including Advanced Analytics Research Scientist, Battery Engineer, Data Scientist/Process Engineer, Deep Learning Research Engineer, Neuroengineer, Senior 3D Process Engineer, and Senior Bioinformatics Quality Engineer.

Experiential Learning (Research, Projects, Internships etc.)

The M.S. in Artificial Intelligence Engineering–Chemical Engineering at Carnegie Mellon University combines AI and chemical engineering through coursework, project work, and research-oriented learning. Students work with AI systems, machine-learning methods, process simulation, numerical modeling, and data-driven approaches to chemical engineering, while the department provides access to advanced computing systems and research facilities in Doherty Hall. The program includes 36 elective units with 12 units of project work, and students may complete a comprehensive research project with a faculty member; a summer internship is also optional.

Students can build practical experience through:

AI and machine-learning tools: Students learn AI systems and toolchains, machine-learning algorithms, deep neural networks, trustworthy AI, and Python-based data analysis.
Process simulation and modeling: The curriculum develops experience with process simulation software, mathematical modeling of chemical processes, chemical and reactive systems, transport modeling, and process systems modeling.
Project-based learning: The program allocates 12 of its 36 elective units to project work, with at least one elective required to contain a significant project component. Students can also complete a comprehensive research project with faculty.
High-performance computing: CMU Chemical Engineering students have access to high-performance computing systems and advanced computing facilities for computational and research work.
Research laboratories: The department's research facilities are located in Doherty Hall and use an open laboratory layout that encourages collaboration and shared use of advanced experimental platforms.
AI and autonomous systems research: The Autonomous and Data-Driven Sciences research area combines machine learning, optimization, and laboratory automation to develop autonomous or semi-autonomous systems for materials, therapies, and processes.
Process systems engineering: Students can engage with research involving mathematical modeling, optimization, machine learning, large-scale process design, and operations, including applications to energy systems and supply chains.
Industry internships: The AIE-ChE program offers an optional summer internship. CMU notes that students must find their own internships and that enrollment does not guarantee one. Recent AIE-ChE students have interned at Los Alamos National Laboratory, Universal Fuel Technologies, and Shell Technology Center, working on chemical process modeling, optimization, computational models, reaction-rate prediction, and experimental-data analysis.
Professional development: Students take 06-608 Graduate Professional Development Seminar and 39-699 Career & Professional Development for Engineering Master's Students, providing dedicated professional-development preparation alongside the technical curriculum.
Collaborative student community: Students can participate in the Chemical Engineering Graduate Student Association (ChEGSA) and Chemical Engineering Master's Student Association (ChEMSA), which provide networking opportunities and events throughout the year.
Career and internship resources: Carnegie Mellon's Career & Professional Development Center (CPDC) supports internship and job searches, while Handshake and the annual STEM Career Fair connect engineering students with employers offering internships and full-time positions.
Research areas connected to the degree: Students can engage with department research spanning biotechnology and pharmaceutical engineering, catalysis and surface science, energy and decarbonization, sustainability, air quality and climate, soft materials, and process systems engineering

Progression & Future Opportunities

The M.S. in Artificial Intelligence Engineering–Chemical Engineering at Carnegie Mellon University prepares graduates to combine chemical engineering expertise with AI, machine learning, data science, and process systems knowledge for careers across advanced engineering and technology-driven industries. CMU reports that 92% of its engineering master’s students who graduated in 2023 and 2024 found employment or continued their education, while Chemical Engineering master’s graduates have gone on to companies including Amgen, ExxonMobil, Intel, Merck, P&G, Samsung, Sanofi, and Unilever.

Typical career roles: Advanced Analytics Research Scientist, Data Scientist/Process Engineer, Deep Learning Research Engineer, Battery Engineer.

Career support: CMU’s Career & Professional Development Center (CPDC) provides career exploration, professional development, experiential-learning and employment assistance. Students can access resources for resumes and cover letters, career fairs, jobs and internships, Handshake, and dedicated guidance for international students.
Employment outcomes: CMU reports that 92% of engineering master’s graduates from 2023 and 2024 were employed or continuing their education. For Chemical Engineering master’s graduates, CMU reports a mean salary of $93,604, based on survey results from program graduates.
Industry connections: Recent CMU Chemical Engineering graduates have joined organizations including Amgen, Deloitte, ExxonMobil, Intel, Merck, P&G, PPG, Samsung, Sanofi, and Unilever. The department also reports career paths such as advanced process control and machine learning engineer, battery modeling engineer, data scientist, process engineer, R&D scientist, and research engineer.
AI and industry relevance: The AIE-ChE curriculum is specifically designed around integrating AI into chemical engineering challenges, including process simulation, process systems modeling, numerical methods, machine learning, and AI-based solutions. This combination gives graduates a distinctive technical profile for data-driven engineering roles.
Accreditation value: CMU’s Chemical Engineering undergraduate program is ABET accredited; the AIE-ChE master's is a graduate program and is not separately identified as ABET-accredited. ABET accreditation therefore applies to the undergraduate Chemical Engineering program rather than this master's degree.
Graduation outcomes: Graduates can enter industry or continue their education, with CMU specifically identifying opportunities in advanced analytics, process engineering, deep learning, battery engineering, bioinformatics, and related AI-enabled engineering fields.

Further Academic Progression: After completing the M.S. in AIE-ChE, students interested in advanced research can pursue a Ph.D. in Chemical Engineering or a related engineering/scientific field. CMU's Chemical Engineering department offers a Ph.D. focused on original research, with doctoral graduates progressing into research scientist, research engineer, AI researcher, data scientist, academic, and national-laboratory careers.