Generative AI Models for Composable Data Management Optimizers
Ref. 2025_002
A master’s student or intern position is available for a candidate with a passion for bringing innovative gen AI techniques into the world of data platforms and composable data management systems, a positive and problem-solving attitude and the willingness to undertake challenging tasks. The candidate will join for a period of 6 months (internship or master’s thesis) the group of AI for Data Integration at IBM Research Zurich.
Composable data management systems have been recently proposed by the databse community as the way forward to decouple user workloads (expressed as SQL, low-code/no-code, Python, natural language, etc.) from the various execution engines underneath. Our team is working to build a cross-engine optimizer that is able to abstract any user workload, independent of its API, and use gen AI methods to learn which engine fits best for that workload while optimizing for cost-performance, and considering dataset statistics, workload characteristics, provisioning, as well as engine configuration. Our team pushes this frontier with a culture of innovation, focusing on the creation and consumption of high-quality science.
Qualifications
- Enrolled or in possession of a Master’s degree in computer science or engineering (for master’s thesis)
- At least a Bachelor’s degree in computer science or engineering (for internship)
- Strong ML/AI knowledge
- Excellent coding skills, Python preferred
- Familiarity with Linux environments and software development tools (git/GitHub, IDEs, etc.)
- High amount of creativity and outstanding problem-solving ability
Preferred Qualifications
- Experience with graph neural networks
- Experience with Spark, Velox, Presto
- Experience with SQL, cardinality estimation, Substrait
- Excellent oral and written English with good presentation skills
- Strong interpersonal skills and excellent written and verbal communication
Diversity
IBM is committed to diversity at the workplace. With us you will find an open, multicultural environment. Excellent flexible working arrangements enable all genders to strike the desired balance between their professional development and their personal lives.
How to apply
