Lead HPC / CUDA Optimization Engineer

New

EPAMHPC, scientific computing

Opportunity to work remotely within PolandFull-TimeLead

Salary not disclosed

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Job Details

Languages: English proficiency at B2 level or higher
Experience: 8+ years of professional experience with C++ and Python development
Required Skills: PythonFGPA ArchitectureSoftware ArchitectureC++

Have 8+ years of professional experience with C++ and Python development.
Bring deep knowledge of CUDA, GPU architecture, PTX, and memory hierarchy.
Have experience with kernel optimization.
Have experience with GPU profiling, performance analysis, and bottleneck identification.
Have experience developing and optimizing performance-critical, high-load software systems.
Demonstrate strong debugging and software architecture skills.
Be able to lead technical initiatives and collaborate effectively with cross-functional teams.
Have English proficiency at B2 level or higher.
Nice to have: hands-on experience with distributed and multi-GPU computing, including MPI, NCCL, or NVSHMEM.
Nice to have: experience with RTM/FWI, seismic imaging, or geoscience applications.
Nice to have: development experience with HDF5-based data viewing and analysis tools.
Nice to have: familiarity with scientific computing and numerical methods.

Design, develop, and optimize CUDA kernels for large-scale scientific and engineering applications.
Use profiling tools to analyze GPU performance, identify bottlenecks, and improve performance.
Develop and maintain high-performance C++ and Python code for performance-critical, high-load systems.
Collaborate with cross-functional teams to architect scalable and efficient software solutions.
Debug performance and memory issues across GPU architectures and distributed systems.
Research and apply HPC, numerical methods, and GPU computing practices to existing codebases.
Provide technical leadership and guidance on GPU optimization, performance engineering, and software architecture.
Drive engineering best practices and contribute to technical decisions for HPC and GPU-accelerated solutions.

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