Principal Performance Engineer
Cornelis Networks
LinuxPython
PhD Intern, Computer Architecture and Systems Research
NVIDIA
internship
Posted on:
Origin: • 🇺🇸 United States • California, Massachusetts
Visit company websiteSalary
💰 $30 - $94 per hour
Job Level
Entry Level
Tech Stack
Distributed SystemsPython
About the role
- Design and implement novel ideas in GPU and CPU architectures, systems architectures, operating systems, AI systems, and distributed systems that advance computing, graphics, media processing, and related technologies central to NVIDIA's business.
- Collaborate with other team members, teams, and/or external researchers.
- Transfer your research to product groups to enable new products or types of products.
- Deliverable results include prototypes, patents, products, and/or publishing original research.
- Hands-on experience with Computer Architecture and Systems teams; expand career and get practical experience.
Requirements
- Must be actively enrolled in a university pursuing a PhD degree in Computer Science, Electrical Engineering, or a related field, for the entire duration of the internship.
- Depending on the internship, prior experience or knowledge requirements could include the following programming skills and technologies: C, C++, Python, CUDA.
- Strong background in research with publications at top conferences.
- Excellent communication and collaboration skills.
- Potential internships require research experience in at least one of the following areas: Chip-level and System-level Architecture; GPU and Multi-GPU Architecture; Scalable memory systems and new memory technologies; Scalable on-chip and off-chip interconnects; Chip-level and system-level scheduling; Power, performance, and energy-efficiency in large-scale systems; Specialized accelerators for workloads like AI algorithms, crypto algorithms, databases, etc.; Hardware-software co-design; Systems for AI/ML; Systems Infrastructure for large LLM training and inference; System/AI algorithms codesign (e.g., for sparsity); AI/ML for systems (hardware design, code optimization, etc.); ML for EDA; Programming Systems; GPU-accelerated algorithms; Languages and programming models for parallel computing; Optimizing compilers and AI-based performance assistants; Distributed runtime systems; Systems software and operating system interfaces; Optimizing GPU-accelerated workloads; Compilers and code verification; High-Performance Networking and Interconnects; Large-scale GPU networking Toplogies, routing, and congestion control; Networking techniques at the intersection of scale-out and scale-up; VLSI and Electronic Design Automation (EDA); GPU Accelerated EDA.
