Research Engineer in the Division of Science (Computer Science) - Dr. Riyadh Baghdadi
The Research Engineer will join a team at NYU Abu Dhabi, in collaboration with MIT, to enhance the Tiramisu programming framework. This role focuses on developing an automatic code optimization module using machine learning and deep learning techniques to improve performance on various hardware architectures. The engineer will contribute to research with high practical impact and publish findings in leading conferences.
Our research team at NYUAD (New York University Abu Dhabi), in collaboration with MIT (Massachusetts Institute of Technology, USA), is developing a new programming framework called Tiramisu (http://tiramisu-compiler.org/). Unlike existing frameworks, Tiramisu can perform advanced code optimizations that are hard to apply otherwise. Because of this, Tiramisu can generate fast code that outperforms highly optimized code written by expert programmers and can target different hardware architectures (multicore, GPUs, FPGAs, and distributed machines).
In order to achieve the best performance (fastest execution) for a given Tiramisu program, many code optimizations should be applied. Optimizations include vectorization (using hardware vector instructions), parallelization (running loop iterations in parallel), enhancing data locality by fusion, and blocking (i.e., accessing arrays in a way that improves temporal and spatial data locality). A large number of optimizations exist, and choosing which optimization should be used and which should not is important for performance. In some situations, some optimizations are harmful to performance while beneficial in others. Currently, there is no way to help users choose which optimizations should be used. Expert Google programmers usually spend a lot of time trying different optimizations manually to find the best set of optimizations.
The goal of this project is to add support for automatic code optimization in Tiramisu. In particular, we want to use machine learning/deep learning to achieve this. Currently, a basic automatic optimization module that relies on machine learning has been developed, and we want to take that module to the next level. The final product of this project would be a compiler pass that allows Tiramisu to automatically choose which optimization should be used for a given unoptimized program. We want to produce a high-quality technique that can be used by the users of Tiramisu and especially by our partner companies and research labs.
To apply, please send your CV, cover letter, 2 letters of recommendation, research statement and transcripts (with ranking) for the last two years and indicate when you’ll be able to start. Being able to start as early as possible is a plus. Please also indicate any relevant technical skills that you have. If you have any questions, please send an email to Dr. Riyadh Baghdadi at rb4792@nyu.edu
NYU Abu Dhabi is a degree-granting research university with a fully integrated liberal arts and science undergraduate program in the Arts, Sciences, Social Sciences, Humanities, and Engineering. NYU Abu Dhabi, NYU New York, and NYU Shanghai, form the backbone of NYU’s global network university, an interconnected network of portal campuses and academic centers across six continents that enable seamless international mobility of students and faculty in their pursuit of academic and scholarly activity. This global university represents a transformative shift in higher education, one in which the intellectual and creative endeavors of academia are shaped and examined through an international and multicultural perspective. As a major intellectual hub at the crossroads of the Arab world, NYUAD serves as a center for scholarly thought, advanced research, knowledge creation, and sharing, through its academic, research, and creative activities.
Posted June 2, 2026