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AI Accelerators Compiler and Architecture Research Intern: 2025
IBM
Software Engineering, IT, Data Science
Multiple locations
Posted on Oct 11, 2024
Introduction
IBM Research Scientists are charting the future of Artificial Intelligence, creating breakthroughs in quantum computing, discovering how blockchain will reshape the enterprise, and much more. Join a team that is dedicated to applying science to some of today’s most complex challenges, whether it’s discovering a new way for doctors to help patients, teaming with environmentalists to clean up our waterways or enabling retailers to personalize customer service.
Your Role and Responsibilities
IBM Research Scientists are charting the future of Artificial Intelligence, creating breakthroughs in quantum computing, discovering how blockchain will reshape the enterprise, and much more. Join a team that is dedicated to applying science to some of today’s most complex challenges, whether it’s discovering a new way for doctors to help patients, teaming with environmentalists to clean up our waterways or enabling retailers to personalize customer service.
Your Role and Responsibilities
This is for a 2025 summer internship with the following start dates: May – August or June – September for quarter system schools.
Work with a team of world-class researchers to design and implement compiler optimizations for AI accelerators targeting state-of-the-art LLMs (large language models). Develop compiler software for design space exploration to map AI applications to AI accelerators.
- Engage with other researchers to debate and discuss merits of existing and proposed compiler optimizations for AI accelerators.
- Prepare internal reports and presentations to summarize progress/blockers.
- Perform research and propose new concepts in compilers to exercise the computer architecture features.
- Implement the compiler optimizations to generate programs that exercise the computer architecture features.
- Evaluate the performance benefits using the software simulators across different use-cases and determine the hardware and software bottlenecks to improve performance.
- Performa cost-benefit analysis using the cost models and the simulator performance and propose changes to the hardware and software to improve the trade-offs.
Required Technical and Professional Expertise
- Applicants should be PhD & MS students pursuing graduate studies.
- Strong software engineering background, and
- Proficiency in C, C++ and Python programming,
- Proficiency in state-of-the-art deep-learning models (example large-language models for Generative AI applications).
- Strong background in AI accelerators architecture and compilers.
Preferred Technical and Professional Expertise
- Strong software engineering background, and
- Proficiency in C, C++ and Python programming,