Senior CUDA HPC Engineer - Remote & GPU Scaling

EPAM Systems; Polska; Hybride; PLN 180 000 - 300 000; Plein temps

Avantages offerts par ce poste

Health insurance

Multisport card

Shopping vouchers

Employee stock purchase plan (discount

Referral bonuses up to $2,000

Entertainment and relaxation zones at-

Résumé du poste

EPAM Systems, Inc. seeks an experienced HPC / CUDA Optimization Engineer to design, develop, and optimize CUDA kernels for large-scale scientific and engineering applications.

You will analyze GPU performance, implement multi-GPU solutions with MPI/NCCL/NVSHMEM, and collaborate across teams to deliver high-load, high-performance software. Role requires deep CUDA expertise, strong C++/Python skills, and hands-on experience with distributed computing.

Qualifications

• 5+ years of professional C++ and Python development.
• Deep knowledge of CUDA, GPU architecture, PTX, memory hierarchy, and kernel optimization.
• Experience with GPU profiling, performance analysis, and bottleneck identification.
• Hands-on experience with distributed and multi-GPU computing (MPI, NCCL, NVSHMEM).
• Experience developing and optimizing performance-critical, high-load software systems.

Responsabilités

• Design, develop, and optimize CUDA kernels for large-scale simulations and engineering applications.
• Analyze and improve GPU performance using profiling tools to identify and resolve bottlenecks.
• Implement and optimize distributed and multi-GPU computing solutions using MPI, NCCL, and NVSHMEM.
• 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 complex performance and memory issues across GPU architectures and distributed systems.

Connaissances

C++

Python

CUDA

GPU optimization

Profiling

MPI

NCCL

NVSHMEM

HPC

Debugging

Outils

CUDA Toolkit

HDF5

Description du poste

EPAM Systems, Inc. seeks an experienced HPC / CUDA Optimization Engineer to design, develop, and optimize CUDA kernels for large-scale scientific and engineering applications.

You will analyze GPU performance, implement multi-GPU solutions with MPI/NCCL/NVSHMEM, and collaborate across teams to deliver high-load, high-performance software. Role requires deep CUDA expertise, strong C++/Python skills, and hands-on experience with distributed computing.