Research Software Engineer for AI Accelerator’s Software Stack

Job description

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

As a Research Software Engineer at IBM Research, you will play a pivotal role in driving the development, testing, and implementation of cutting-edge AI and Cloud technologies. In this specific position, your responsibilities will span across various aspects of software engineering, including design, construction, and evaluation of software technologies, such as compilers, runtime components, quantization algorithms, and supporting tools, to enable the use of AI accelerators. In this role, you will have the opportunity to work on exciting research initiatives, assess the potential applications of emerging AI and Cloud technologies, and showcase the value of these technologies to both IBM's businesses and strategic partners. You will be part of a diverse and inclusive team of researchers and engineers, working together to push the boundaries of Cloud-based AI platforms.

**Todas as nossas vagas são elegíveis para pessoas com deficiência ou reabilitadas.**

Required Technical and Professional Expertise

·  Strong programming skills in commonly used system programming languages, such as C, C++, and Rust.
·  Demonstrated experience in developing and testing system software, such as compilers, runtimes, quantization algorithms, debuggers, profilers, performance analysis tools, etc.
·  Strong communication skills.
·  Fluent level of English

Preferred Technical and Professional Expertise

·  Demonstrated experience with GPU and TPU architectures.
·  Demonstrated experience with LLVM, CUDA and Triton compilers.
·  Demonstrated experience with GPTQ/AWQ quantizers.
·  Demonstrated experience with development of PyTorch hardware backends.