George Washington University

How this program compares

George Washington University's Doctor of Engineering in AI and Machine Learning lists about $84K total tuition — roughly 4% below the $87.2K AI doctoral average (64th percentile in our data). It is one of the 54% of programs in our database offered fully or partly online.

Admission Snapshot

Typical admitted student: A master's degree in engineering, computer science, or a related field with a minimum GPA of 3.0 is required, along with relevant professional experience in AI or machine learning and strong quantitative background including linear algebra, probability, and programming.

About This Program

This research-based doctoral program provides advanced expertise in AI and machine learning, focusing on applying cutting-edge techniques to solve real-world engineering challenges. Coursework concentrates on Generative AI and LLMs and Natural Language Processing (NLP). Most students complete it in about 2 years.

Budget planners should note $84K estimated total tuition (64th percentile among AI doctoral programs, below the $87.2K average). That puts it in the mid-range on price among comparable programs.

It is one of 15 AI-related programs we track in District of Columbia, of which about 60% offer an online option. On price, it comes in higher than about 73% of the District of Columbia programs in our data, where typical tuition runs near $83.4K.

Graduates frequently move into roles such as AI Research Scientist, with reported salaries around $180,000.

Design and deploy scalable AI systems for engineering applications.

Career Outcomes

George Washington University positions this doctoral track for AI Research Scientist careers — typical offers cluster around $180,000. Its focus on Generative AI and LLMs and Natural Language Processing (NLP) maps directly to how employers in engineering ai screen for specialized skills rather than generic degrees. Federal projections for this occupational area point to roughly 23% growth this decade — verify the current figure on the BLS Occupational Outlook Handbook before you rely on it.

• 1. AI Systems Architect
• 2. Machine Learning Director
• 3. Principal AI Engineer
• 4. Research Engineer in AI

What You'll Learn

• Design and deploy scalable AI systems for engineering applications.
• Develop advanced machine learning models for real-world data challenges.
• Apply deep learning techniques to computer vision and natural language processing.
• Engineer ethical and robust AI solutions integrating engineering principles.

Curriculum Highlights

The 48-credit program consists of 24 credits of required coursework in subjects like reinforcement learning and analytical methods, followed by 24 credits of praxis research.

Top Employers

Top employers include tech leaders like Google, Amazon, Microsoft, and engineering firms such as Lockheed Martin and Boeing.

Admissions

Admission to George Washington University's Doctor of Engineering in AI and Machine Learning generally expects a master's degree. The GRE is optional here — a growing norm among AI programs — so applicants can often lead with coursework, projects and recommendations instead. Deadlines, testing policies and funding change year to year, so confirm the current requirements on the official program page before applying.

Application Materials

• Statement of Purpose: Required
• Letters of Recommendation: 3
• Resume: Required
• Transcripts: Official transcripts required

Frequently Asked Questions