Qualcomm International Inc., Mexico Branch Office

Machine Learning & Artificial Intelligence Engineering Internship – Summer 2027 at Qualcomm International Inc., Mexico Branch Office

Department: Interim Engineering Intern - SW

Compensation

$17 – $98/hr

Setup: On-site
Location: San Diego, California · Austin, Texas · +5
Type: Internship
Level: intern
Posted: Sep 18, 2026

Description

Qualcomm is recruiting Machine Learning/AI Engineering interns for its 2027 summer program, with an 11–14-week commitment from May through September. The intern will help build software and tools for mobile and embedded machine learning platforms, including neural network frameworks, inference systems, compiler technologies, GPU acceleration, model serving, and performance optimization. The role requires enrollment in a relevant degree program, availability during the specified summer, and at least one year of programming experience; preferred qualifications include deep learning, ML frameworks, compiler knowledge, and AI infrastructure experience. Internships may be available in Austin, Boulder, Boxborough, Bridgewater, Chandler, Irvine, New York City, Louisville, Raleigh, San Diego, Santa Clara, and Seattle, with compensation listed at $17.00–$98.00.

Required Skills

C/C++/Python
Deep learning
ML frameworks
Neural networks
Image processing
Compiler frameworks
GPU programming
CUDA
Kubernetes
Performance optimization

Additional Information

Minimum 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
Required Previous Experiences: At least 1 year of experience with C, C++, or Python
Preferred Qualifications: Currently enrolled in a master’s or Ph.D. degree program in a related field
Degree completion anticipated between November 2027 and June 2028
Proficiency in deep neural networks and machine learning architectures
Experience with TensorFlow, TFLite, or PyTorch
Neural network programming and video/image processing
Compiler frameworks and parallel computing
AI infrastructure, inference systems, distributed computing, GPU programming, CUDA, Kubernetes, MLIR, or performance optimization
Linear algebra and fast math libraries
Theoretical knowledge of machine learning, deep learning, model compression, quantization, and optimization
Reinforcement learning, neural architecture search, kernel optimization, or Bayesian optimization
On-device training, transfer learning, personalization, federated learning, NLP, or ML security/privacy
Deep generative models, audio/speech processing, NLP, computer vision, or wireless communication
Machine learning data pipelines, data management, or backend/frontend applications
Research publications in NeurIPS, CVPR, ICML, ICLR, or ICCV
Object-oriented software design