- Company Name
- Qualcomm
- Job Title
- Machine Learning & Artificial Intelligence Engineering Internship – Summer 2026
- Job Description
-
**Job Title:**
Machine Learning & Artificial Intelligence Engineering Internship – Summer 2026
**Role Summary:**
An 11–14 week internship focused on designing, developing, and testing software tools and frameworks that enable efficient machine learning on mobile and embedded platforms. Interns work with neural network libraries, compiler infrastructures, and Qualcomm’s SOC accelerators to produce models that are small, fast, and secure for edge devices.
**Experiences:**
- Current enrollment in bachelor’s, master’s, or Ph.D. program in computer engineering, computer science, electrical engineering, or related field.
- Availability for full summer term (May–September) 2026.
- Expected graduation in November 2026 or later.
- Minimum 1 year of programming experience in C, C++, or Python.
**Key Responsibilities:**
- Develop software components for machine‑learning tools and frameworks aimed at model compression, quantization, and optimization.
- Integrate and benchmark neural network models on Qualcomm SOC hardware accelerators.
- Participate in end‑to‑end ML pipeline design, including data preprocessing, training, on‑device inference, and profiling.
- Collaborate with a multidisciplinary team of researchers, software developers, and hardware engineers.
- Contribute to internal documentation, test suites, and continuous‑integration workflows.
- Communicate findings and progress to project leads and peers.
**Required Skills:**
- Proficient in C, C++, and Python programming.
- Experience with deep learning frameworks (TensorFlow, TensorFlow Lite, PyTorch).
- Understanding of neural network architectures (CNN, RNN, LSTM) and core ML concepts.
- Familiarity with compiler and code‑execution frameworks (LLVM, GCC, TVM, XLA).
- Knowledge of linear algebra, fast‑math libraries, and parallel computing principles.
- Basic familiarity with model compression, quantization, and optimization techniques.
- Ability to write clean, object‑oriented code and maintain version‑controlled contributions.
**Preferred Qualifications**
- Enrollment in a master’s or Ph.D. program with anticipated graduation between November 2026 and June 2027.
- Exposure to advanced topics such as neural architecture search, Bayesian optimization, reinforcement learning, or federated learning.
- Prior experience in video/image processing, NLP, or audio/speech ML.
- Publication record in machine‑learning conferences (NeurIPS, CVPR, ICML, ICLR, ICCV).
**Required Education & Certifications:**
- Current bachelor’s, master’s, or Ph.D. student in computer engineering, computer science, electrical engineering, or closely related discipline.
- No specific certifications required; coursework in machine learning, deep learning, linear algebra, and systems programming is expected.