Solutions Engineer

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.

As a Performance Engineer - Systems Benchmarking & Auto-Tuning at Daisytuner, you will operate at the intersection of compiler technology, performance engineering, and real-world high-performance workloads. You will not only validate our compiler on demanding applications but also analyze bottlenecks, design optimizations, and implement transformations that become part of our autotuning pipeline.

What you will be doing

• • Act as the technical bridge between real-world workloads and our compiler infrastructure
• • Set up, build, and benchmark performance-critical applications (e.g., Machine Learning, Computational Fluid Dynamics, High-Frequency Trading, scientific computing)
• • Analyze C/C++ and CUDA codebases to identify bottlenecks at the algorithmic, memory, and microarchitectural level
• • Use profiling and performance analysis tools to understand system behavior
• • Design and implement optimization strategies (loop transformations, tiling, data layout changes, memory hierarchy optimizations, parallelization strategies)
• • Develop and integrate compiler transformations that automate identified optimizations within our autotuning framework
• • Design reproducible benchmarking setups and performance evaluation methodologies
• • Translate customer requirements into concrete technical experiments, optimization tasks, and transformation strategies
• • Provide structured feedback that directly influences compiler design and optimization heuristics

What you are bringing

• • Master's degree (or equivalent experience) in Computer Science, Mathematics, Physics, or a related technical field
• • Strong C/C++ skills and the ability to read and modify complex performance-critical codebases
• • Solid understanding of performance engineering fundamentals such as cache hierarchies and memory bandwidth, vectorization and SIMD, and parallel programming (OpenMP, CUDA, or similar)
• • Ability to reason about performance trade-offs and optimization strategies systematically
• • Experience setting up reliable benchmarks and interpreting performance measurements
• • Proficiency in Python for automation and experimentation
• • Comfortable working in Linux environments and with Git-based workflows
• • Strong communication skills in English and/or German
• • A structured, analytical working style and willingness to take ownership in an early-stage environment
• • Experience in at least one performance-critical domain such as ML systems, HPC, finance, simulation, or databases(plus)
• • Experience with profiling and analysis tools such as perf, Nsight, VTune, or PAPI(plus)
• • Familiarity with compiler infrastructures or intermediate representations(plus)

What we offer

• • A small, highly technical team with direct impact on core technology
• • Competitive compensation and potential equity participation
• • The opportunity to work at the intersection of compilers, machine learning, and high-performance computing
• • Real ownership over optimization strategies that ship into production systems