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Introduction
The job responsibilities include working with a team of world-class researchers to help define and develop software stacks, tools and methodologies that enable cutting edge research in Deep Learning related projects. The primary mission for the team is to lead the definition of various architectures and the compiler stack for new hardware accelerators for Deep Learning. Key responsibilities will include proposing new concepts in software that applies compilers to exercise the hardware architecture features of AI accelerators, implementing optimizations to generate programs and evaluating performance benefits using the software simulators across different use-cases, determining hardware/software techniques to improve performance. Further responsibilities include performing research and development of novel software solutions and evaluating their merits relative to state-of-the-art solutions and demonstrating external eminence by publishing the outcomes of the research.
Your role and responsibilities
Are you eager to transform groundbreaking research into real-world solutions? Do you have a passion for developing cutting-edge AI systems that drive meaningful impact? Would you enjoy contributing to open-source advancements?
If so, we invite you to apply for our Research Scientist position at IBM. We are seeking highly motivated candidates with expertise in Large Language Models (LLMs) and a passion for software engineering and machine learning. As part of our world-class team, you will explore and work with novel techniques to enhance AI model training and deployment.
Your work will contribute to software for compiler stacks for next-generation hardware accelerators along with algorithmic breakthroughs for AI applications across multiple domains. You will collaborate with a diverse team of researchers specializing in software engineering, algorithm optimizations, hardware architecture, graph compilers, AI frameworks, and applications.
Required education
Master's Degree
Preferred education
Doctorate Degree
Required technical and professional expertise
- Strong software engineering background and proficiency in C, C++ and Python programming as well as experience with state-of-the-art software collaboration practices is essential.
- Strong background in architecture and compilers with experience developing simulators and compilers.
- Familiarity with a variety of state-of-the-art AI model architectures.
Preferred technical and professional experience
- Experience with the use and adaptation of Deep Learning frameworks including TensorFlow and PyTorch as well as CUDA for GPU programming is preferred.
- Strong background in deep-learning applications including large language models.