GPU Software Engineer; HPC​/Deep Learning Optimization

Project description

We are looking for a Software Engineer focused on GPU computing, HPC workloads, and Deep Learning inference optimization on Windows platform. The project is aimed at improving performance and efficiency of GPU-based workloads, including compute kernels and inference pipelines. The role is not limited to graphics APIs and is suitable for candidates with strong experience in CUDA, OpenCL, or similar technologies, as well as shader-based optimization.

• Develop, optimize, and maintain GPU compute kernels using C++ and a GPU programming framework (CUDA, HIP, OpenCL, SYCL, Direct Compute / HLSL compute shaders, Metal compute, or equivalent).
• Profile GPU workloads and tune memory, compute, and latency to improve performance and efficiency.
• Analyze performance bottlenecks and apply targeted optimizations.
• Debug and resolve performance and stability issues.
• Apply kernel optimization to HPC or Deep Learning inference pipelines where needed.
• Collaborate with engineers, QA, and stakeholders.
• Follow coding standards and contribute to technical documentation.

• Hands on experience writing and optimizing GPU compute kernels in at least one framework: CUDA, HIP, OpenCL, SYCL, Direct Compute / HLSL compute shaders, Metal compute, or equivalent.
• Ability to profile and performance tune GPU code (memory, compute, latency) as part of that work.

• Strong knowledge of C++
• Experience with HPC or Deep Learning inference optimization
• Experience with profiling tools (Nsight, Radeon GPU Profiler, PIX, etc.)
• Experience with Deep Learning frameworks (TensorRT, ONNX Runtime, PyTorch, DirectML, etc.)
• Understanding of graphics pipelines and rendering basics
• Experience with a graphics API (DirectX, Vulkan, Metal, etc.)
• Experience with Windows platform GPU development
• Experience with CI/CD, version control, or automated testing