Machine Learning Engineer - Compiler Optimization (m/w/d)

About Daisytuner

Daisytuner is building the software layer for the next generation of computing. We make complex software run efficiently on any processor from CPUs and GPUs to novel accelerators using a self-learning compiler and cloud‑scale optimization infrastructure.

Our team brings together researchers and engineers from RWTH Aachen, TU Munich, TU Darmstadt, and ETH Zurich to tackle some of the hardest problems in systems and infrastructure software. If you want to work on deeply technical challenges with real‑world impact, join us and help shape the future of compute.

Role

Machine Learning Engineer – You will work at the intersection of compilers, hardware, and performance. Your work will help us build intelligent systems that understand complex program behavior and improve how software runs on modern processors. You will apply machine learning to structured program representations, compiler transformations, and real‑world performance measurements.

What you will be doing

• Develop and improve machine learning models that guide compiler optimization and search strategies
• Formalize optimization problems as learning tasks, including defining meaningful features, targets, and evaluation metrics
• Work with compiler IR, program structure, and performance data
• Design and implement data pipelines for collecting, processing, and validating performance measurements
• Systematically evaluate model variants based on real‑world speedups and robustness across hardware targets
• Handle noisy and hardware‑dependent performance signals in a principled way
• Collaborate closely with compiler and infrastructure engineers to integrate models into production systems

What you are bringing

• Strong background in Computer Science, Performance Engineering, Compilers, or a related systems field (degree or equivalent experience)
• Solid understanding of performance engineering concepts, compiler optimization, or program analysis
• Expertise in applying machine learning methods in practical settings (this can include academic or industry projects)
• Understanding of model evaluation methodology and experimental rigor
• Strong programming skills in Python and/or C++
• Interest in using ML to solve systems and compiler optimization problems
• Strong communication skills in English (German is a plus)
• A pragmatic, structured working style and willingness to take ownership in an early‑stage environment

Equality Statement

We are an equal opportunity employer and welcome applications from people of all backgrounds. We value diversity and believe that different perspectives make us stronger. We do not discriminate based on gender, nationality, ethnic origin, religion, disability, age, sexual orientation, or identity.

Job Details

• Job Types: Full-time, Permanent
• Work Location: Hybrid remote in Darmstadt