Qualcomm
Modular Engineering Internship – Summer 2027
Los Altos, California · On-site · Internship · Posted Oct 2, 2026
- Experience
- 1+ years of academic or project experience with programming languages; no other specific duration stated.
- Education
- Currently enrolled in a bachelor’s, master’s, or Ph.D. degree program in computer science, computer engineering, electrical engineering, software engineering, mathematics, artificial intelligence, or a related technical field.
- Visa sponsorship
- The role may require an export license for candidates with citizenship in certain listed countries; if required, work cannot start until the license is granted.
About the job
Contributes to Modular’s AI inference software stack through project work in areas such as model optimization, compilers, GPU kernels, distributed systems, security, quality engineering, and developer tools.
What you'll do
- Develop or optimize AI inference models, software, compilers, runtimes, GPU kernels, or accelerator workloads.
- Build or improve AI infrastructure, model-serving platforms, distributed systems, quality frameworks, or developer tools.
- Work on a project aligned with the intern’s background, interests, and team needs.
What they ask for
- Currently enrolled in a bachelor’s, master’s, or Ph.D. program in a listed or related technical field.
- Available for 11–14 weeks during Summer 2027 (May–September).
- Expected graduation date of November 2027 or later.
- 1+ years of academic or project experience with Python, C, C++, Rust, Go, or related programming languages.
Nice to have
- Experience in relevant areas such as AI/ML, compilers, GPU programming, distributed systems, software engineering, security, or developer tools.
- Research experience or publications in relevant AI/ML, systems, compiler, or engineering areas.
Pay
$17.00 - $98.00 (currency and hourly or salary basis not stated)
Tools
Python, C, C++, Rust, Go, PyTorch, TensorFlow, Hugging Face
AI focus
Develops and optimizes AI inference infrastructure, models, and software, potentially including model serving, GPU and accelerator performance, compilers, distributed AI systems, evaluation, and AI security.
Model-written summary · the original posting is authoritative