Research Software Engineer For Cloud-Based Ai Platforms
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IBM Research Scientists are mapping the future of Artificial Intelligence, creating breakthroughs in quantum computing, discovering how blockchain will reshape the enterprise, and more. Join a team dedicated to applying science to some of today's most complex challenges, whether it's discovering a new way for doctors to help patients, joining with environmentalists to clean up our rivers and seas, or enabling retailers to customize the customer service.
Your role and responsibilities
As a research software engineer at IBM Research, you will play a key role in driving the development, testing, and implementation of cutting-edge AI and cloud technologies. In this specific position, your responsibilities will encompass multiple aspects of software engineering, including designing, building, and evaluating software technologies to advance our cloud-based AI platform. In this role, you will have the opportunity to work on exciting research initiatives, evaluate the potential applications of emerging AI and cloud technologies, and showcase the value of these technologies to IBM's business 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.
All vacancies are eligible for people with disabilities or rehabilitated people.
Required Professional and Technical Knowledge
- Strong programming skills in commonly used programming languages such as Java, Golang, Python, C++ and Rust.
- Proven experience developing and testing middleware or cloud-based distributed systems.
- Proven experience with Github, CI/CD and other DevOps technologies.
- Strong communication skills.
- Fluent English
Preferred Professional and Technical Knowledge
- Proven experience with OpenShift/Kubernetes and related technologies such as platform observability, minikube, Kubernetes Operator SDK, OLM (Operator Lifecycle Manager).
- Proven experience with PyTorch and related technologies.
- Proven experience in end-to-end testing of cloud-based systems.
- Proven experience with GPTQ/AWQ quantizers.
- Proven experience with developing PyTorch hardware backends.
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