IBM Research takes responsibility for technology and its role in society. Working in IBM Research means you'll join a team who invent what's next in computing always choosing the big urgent and mind-bending work that endures and shapes generations. Our passion for discovery and excitement for defining the future of tech is what builds our strong culture around solving problems for clients and seeing the real world impact that you can make.
IBM's product and technology landscape includes Research Software and Infrastructure. Entering this domain positions you at the heart of IBM where growth and innovation thrive.
Conduct research design development and optimization of software platforms that enable the creation deployment and management of artificial intelligence and machine learning models including LLMs. This includes the development of frameworks tools and infrastructure that support the entire AI lifecycle. The AI Platform speciality requires a deep understanding of AI and machine learning algorithms software development and cloud computing as well as expertise in areas such as developing and optimizing the AI software stack including components such as data ingestion and processing model architectures and compilation training serving and monitoring to support the creation and deployment of AI applications and agentic workloads.
- BSc in Computer Science or a related field with a focus on artificial intelligence machine learning or software development.
- Strong background in artificial intelligence and machine learning including expertise in areas such as deep learning LLMs and foundation models.
- Strong background in software development including proficiency in languages such as Python Go or C++.
- Experience with containerization using Docker Kubernetes or other container orchestration tools.
- Research and development experience in AI platforms including the design implementation and optimization of AI frameworks tools compilers or infrastructure.
- Developing and optimizing the AI software stack including components such as data ingestion and pre-processing model training model serving and monitoring.
- Design and development of cloud-based AI platforms that leverage cloud computing resources including compute storage and networking.
- Collaboration with research and engineering teams to advance the state-of-the-art in AI platforms and AI model architectures. Drive innovation in the field as evident from publications patents or opensource activity.