Machine Learning & Artificial Intelligence Engineering Internship – Summer 2027
About The Position
As a Machine Learning / AI Engineering Intern, you will help build best-in-class solutions and tools that enable state-of-the-art technologies for next-generation mobile and embedded machine learning platforms. These platforms power smartphones, autonomous vehicles, robotics, and IoT devices. You’ll work in a dynamic research environment as part of a multi-disciplinary team of researchers and software developers, collaborating with internal teams, working with popular neural network frameworks, and gaining exposure to Qualcomm’s SOC compute and ML hardware accelerators. Projects may also focus on AI systems infrastructure, distributed inference platforms, compiler technologies, runtime systems, GPU acceleration, model serving, performance optimization, and large-scale deployment of machine learning workloads. You will design, develop, and test software for machine learning tools and frameworks that make models smaller and more efficient for edge devices. This internship supports the Machine Learning/ AI Engineering track. Based on your resume you will be aligned to projects within this track.
Requirements
- Currently enrolled in a bachelor’s, master’s, or Ph.D. degree program in computer engineering, computer science, electrical engineering, or a related field
- Must be 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 Haves
- Currently enrolled in a Master’s or PhD degree program in computer engineering, computer science, electrical engineering, or a related field
- Candidates actively pursuing a degree with an anticipated graduation between November 2027 and June 2028
- Proficiency in deep neural networks, machine learning algorithms, and architectures (CNNs, RNNs, LSTMs)
- Experience with ML frameworks like TensorFlow, TFLite, PyTorch
- Skills in neural network programming, video/image processing, and application development
- Knowledge of compiler frameworks (LLVM, GCC, TVM, XLA) and parallel computing
- Experience with AI infrastructure, inference systems, distributed computing, runtime systems, GPU programming, CUDA, Kubernetes, MLIR, or performance optimization of machine learning workloads.
- Understanding of linear algebra operations and fast math libraries
- Theoretical knowledge of ML, deep learning, model compression, quantization, and optimization
- Experience with reinforcement learning, neural architecture search, kernel optimization, Bayesian optimization
- Familiarity with on-device training, transfer learning, personalization, federated learning, NLP, and ML security/privacy
- Experience with deep generative models, audio/speech processing, NLP, computer vision, and wireless communication
- Background in ML data pipelines, data management, backend/frontend applications
- Research excellence with publications in NeurIPS, CVPR, ICML, ICLR, ICCV
- Experience in object-oriented software design (OOSD)
Responsibilities
- Design, develop, and test software for machine learning tools and frameworks that make models smaller and more efficient for edge devices.
Benefits
- competitive hourly pay
- accrued vacation time
- relocation coverage
- furnished housing accommodations
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What This Job Offers
Job Type
Full-time
Career Level
Intern
