Research Scientist: Compiler Optimization & MLIR

Google; Paris; Sur place; EUR 130 000 - 133 000; Plein temps

Résumé du poste

Google Research in Paris seeks a Research Scientist to advance compiler optimizations and machine learning approaches within a high-impact research program. You will explore new architectures, publish findings, and collaborate across teams to drive practical, scalable solutions.

You will help transition concepts into production ideas, contribute to MLIR-based projects, and work with Cloud R&D and hardware teams to improve AI system efficiency and performance.

Qualifications

• PhD in Computer Science, a related field, or equivalent practical experience.
• 4 years of experience working with compilers and performance optimization.
• 4 years of experience programming in C++.
• 4 years of experience with research agendas across multiple teams or projects.
• One or more scientific publication submissions for conferences, journals, or public repositories (such as CVPR, ICCV, NeurIPS, ICML, ICLR, etc.).

Responsabilités

• Implement software functionality to transition compiler research concepts into practical implementations.
• Design new compiler optimizations and algorithms, and author research publications for academic venues.
• Contribute to specialized machine learning compiler frameworks, including EnzymeAD and Reactant.
• Contribute directly to upstream Multi-Level Intermediate Representation (MLIR) open-source projects.
• Execute code integrations from upstream MLIR into internal software repositories.

Connaissances

C++ programming

Compiler research

Research publications

Formation

PhD in Computer Science or related field

Outils

MLIR

EnzymeAD

Reactant

Description du poste

Google Research in Paris seeks a Research Scientist to advance compiler optimizations and machine learning approaches within a high-impact research program. You will explore new architectures, publish findings, and collaborate across teams to drive practical, scalable solutions.

You will help transition concepts into production ideas, contribute to MLIR-based projects, and work with Cloud R&D and hardware teams to improve AI system efficiency and performance.