Senior HPC Performance Engineer — GPU-Accelerated Tuning

NVIDIA; California; On-site; USD 184,000 - 288,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA's NVHPC compilers & tools group seeks an experienced HPC analytics specialist to analyze and run HPC applications on specialized servers, identifying optimization opportunities for compiler development and application engineering teams.

You will work with synthetic benchmarks and full-scale distributed-memory workloads on heterogeneous systems, collaborating with engineers to improve performance and scalability across CPU, GPU, and many-core architectures.

Qualifications

• BS/MS or equivalent experience in Computer Science or related engineering field.
• 8+ years of programming experience.
• Solid understanding of Fortran/C/C++, as well as programming techniques, especially for parallel architectures; preferably for compilers.
• Experience with OpenACC, OpenMP, MPI, and CUDA.
• Strong skills in performance analysis and tuning, as well as a broad understanding of parallel applications development tools and runtime environments.
• Strong mathematical fundamentals, including linear algebra and numerical methods.
• Understand performance considerations, tradeoffs and impact.
• Excellent written and verbal communication skills in a dynamic product oriented team.

Responsibilities

• Assist customers to GPU accelerate HPC applications.
• Analyze HPC applications to better understand performance characteristics.
• Provide advice and drive compiler and applications engineering development teams based on analysis.

Skills

Fortran/C/C++

Parallel architectures

OpenACC/OpenMP/MPI/CUDA

Performance analysis

Communication skills

Problem solving

Education

BS/MS in Computer Science or related engineering field

Job description

NVIDIA's NVHPC compilers & tools group seeks an experienced HPC analytics specialist to analyze and run HPC applications on specialized servers, identifying optimization opportunities for compiler development and application engineering teams.

You will work with synthetic benchmarks and full-scale distributed-memory workloads on heterogeneous systems, collaborating with engineers to improve performance and scalability across CPU, GPU, and many-core architectures.