Qualcomm
Machine Learning & Artificial Intelligence Engineering Internship – Summer 2027
San Diego, California +6 · On-site · Internship · Posted Sep 18, 2026
- Location
- San Diego, California
Austin, Texas
Boxborough, Massachusetts
New Jersey
New York City, New York
Raleigh, North Carolina
Santa Clara, California - Experience
- 1+ years of programming experience.
- Education
- Currently enrolled in a bachelor’s, master’s, or Ph.D. degree program in computer engineering, computer science, electrical engineering, or a related field.
- Visa sponsorship
- An export license may be required for candidates with citizenship in certain listed countries; if required, work cannot start until the license is granted.
About the job
Develop and test software for machine-learning tools and frameworks that make models smaller and more efficient for mobile and embedded edge devices.
What you'll do
- Build solutions and tools for next-generation mobile and embedded machine-learning platforms, collaborating with researchers, software developers, and internal teams.
- Design, develop, and test software for machine-learning tools and frameworks.
- Work on projects that may include AI infrastructure, inference, compilers, runtime systems, GPU acceleration, model serving, and performance optimization.
What they ask for
- Currently enrolled in a bachelor’s, master’s, or Ph.D. program in computer engineering, computer science, electrical engineering, or a related field.
- Available for 11–14 weeks during Summer 2027 (May–September).
- Expected graduation date of November 2027 or later.
- 1+ years of experience with programming languages such as C, C++, Python.
Nice to have
- Currently enrolled in a Master’s or PhD program in a related field.
- Experience or proficiency in machine learning, deep learning, frameworks, AI systems, or related areas listed in the posting.
Pay
$17.00 - $98.00 (hourly pay; the posting does not state an ISO currency code)
Tools
C, C++, Python, TensorFlow, TFLite, PyTorch, LLVM, GCC
AI focus
Machine-learning and AI engineering for mobile and embedded platforms, including edge model efficiency; projects may involve inference systems, model serving, AI infrastructure, compiler and runtime technologies, GPU acceleration, and workload optimization.
Model-written summary · the original posting is authoritative