HPC Performance Modeling & Auto-Tuning Engineer

Huawei Switzerland; Zürich; Vor Ort; CHF 120.000 - 190.000; Vollzeit

Benefits dieser Stelle

Competitive salary and benefits

Professional growth opportunities

Innovative projects

Access to state-of-the-art technology

Zusammenfassung

Huawei Technologies Switzerland AG in Zürich seeks a highly skilled researcher to advance performance modeling and micro-benchmarking for AI compute workloads. You will help develop architecture-aware optimizations and cost models for autotuning, extending GraphBLAS backends and enabling multi-node scaling.

The role requires a PhD or MSc and hands-on expertise in C/C++, parallelism, and benchmarking, with opportunities for professional growth and collaboration in cutting-edge AI research.

Qualifikationen

• MSc or PhD in CS, Engineering, or a related technical discipline.
• Strong knowledge of computer architecture and parallel processing (memory hierarchies, OpenMP, NUMA, SIMD).
• Experience in benchmarking & performance engineering for parallel code.
• Research experience in analytical performance modeling, DS/ML workloads, MPI, autotuning, or kernel optimization.

Aufgaben

• System characterization & micro benchmarking — develop a microbenchmark suite for machine model coefficients and coverage of engines.
• Implementation side: Extend current GraphBLAS backends and model application; explore cost-model-driven autotuning.

Kenntnisse

C/C++ programming

Python

OpenMP/SIMD parallel processing

Benchmarking & performance engineering

Research experience/analytical methods

Ausbildung

MSc or PhD in Computer Science, Engineering, or a related technical discipline

Tools

GraphBLAS

MPI

GPU/accelerator programming

BLAS/GEMM

Jobbeschreibung

Huawei Technologies Switzerland AG in Zürich seeks a highly skilled researcher to advance performance modeling and micro-benchmarking for AI compute workloads. You will help develop architecture-aware optimizations and cost models for autotuning, extending GraphBLAS backends and enabling multi-node scaling.

The role requires a PhD or MSc and hands-on expertise in C/C++, parallelism, and benchmarking, with opportunities for professional growth and collaboration in cutting-edge AI research.