Duke University
Master of Engineering in Artificial Intelligence
Last reviewed June 2026 by the AI Graduate editorial team. Program data is compiled and verified from official university sources — see our methodology.
How this program compares
At an estimated $102.9K in total tuition, the Master of Engineering in Artificial Intelligence sits about 143% above the $42.4K average for AI master's programs in our database — placing it in the 96th percentile on cost among the 531 we track at this level. It is one of the 57% of programs in our database offered fully or partly online.
Admission Snapshot
Typical admitted student: Requires a bachelor's degree in computer science, engineering, or a related STEM field with a minimum GPA of 3.0, programming experience, and coursework in probability, statistics, and linear algebra. GRE is optional; relevant internships or projects strengthen applications.
About This Program
Duke's Master of Engineering in Artificial Intelligence blends technical AI coursework with management and product training, preparing graduates to lead AI initiatives rather than only build models. The curriculum covers machine learning, deep learning, and AI product development.
The program suits engineers who want to move into AI leadership and product roles, combining Duke's brand with a practical, industry-aligned structure.
Why students choose it: Technical AI plus product/management training for future AI leaders.
Career Outcomes
Lead as Machine Learning Engineer in Business AI with machine learning expertise
- 1. Machine Learning Engineer
- 2. AI Product Manager
- 3. Data Scientist
- 4. AI Research Engineer
What You'll Learn
- Build and deploy machine learning models for real-world AI applications.
- Apply deep learning techniques to computer vision and natural language processing.
- Develop AI strategies integrating product innovation and ethical considerations.
- Execute team-based AI projects simulating industry consulting scenarios.
Curriculum Highlights
The program is structured with four technical AI/ML courses, three product development courses, three technical electives for career specialization, and a significant industry-based internship or project.
Top Employers
Top employers include Google, Microsoft, Amazon, Meta, and consulting firms like McKinsey and BCG.
Admissions
Requires a bachelor's degree in computer science, engineering, or a related STEM field with a minimum GPA of 3.0, programming experience, and coursework in probability, statistics, and linear algebra. GRE is optional; relevant internships or projects strengthen applications.
Application Materials
- Statement of Purpose: Required
- Letters of Recommendation: 3
- Resume: Required
- Transcripts: Official transcripts required
Academic Requirements
- Degree Required: Bachelor's degree
- GRE/GMAT: Optional
- TOEFL/IELTS: Required for international students (TOEFL 90+ / IELTS 7.0+)
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