🔎GPU Software Engineer (Graphics / ML)🔎

🟦Join us in Luxoft!

(opportunity for PL based candidates )

💰 Paid Referrals

💻Equipment

🩺Private Medical & Dental care & Life Insurance (covered by us on Employment contract )

🏋🏽 MyBenefit program (sports card, well-being program etc.) (covered by us on Employment contract )

🌎 Internal Mobility program - possibility of rotation between projects, locations, accounts

🎓 LuxTalent platform (webinars, training, courses)

… and many more!

🟦Project Description:

We are looking for engineers to join a GPU software team working at the intersection of real-time graphics and machine learning (upscaling, denoising, artifact suppression for interactive visual applications). The work spans rendering pipelines, ML model integration and GPU performance optimization, in collaboration with graphics and driver teams.

🟦Responsibilities:

Develop and optimize rendering and/or ML inference components for real-time visual pipelines (DX12, Vulkan, ONNX-based stacks).

Profile GPU workloads and tune for latency, memory and throughput.

Integrate ML models (super-resolution, denoising) into graphics pipelines.

Evaluate output quality using objective and perceptual metrics (PSNR/SSIM, LPIPS) and visual regression tooling.

Author clean, testable, reproducible code; collaborate with graphics, ML and platform teams.

🟦Mandatory Skills Description:

4+ years of software engineering experience with C++ (for ML-focused candidates: strong Python with working-level C++).

Solid GPU fundamentals: pipeline, synchronization and memory models, performance trade-offs.

Deep expertise in at least one of the two areas:

(a) real-time graphics: DX12 and/or Vulkan, shader authoring (HLSL/GLSL), rendering techniques, GPU debugging/profiling (RenderDoc, PIX, Radeon GPU Profiler), OR

(b) image ML: PyTorch, super-resolution/denoising/artifact-suppression models, inference deployment and optimization on GPU (ONNX Runtime or TensorRT, quantization).

Working awareness of the other area: ability to integrate a pre-trained model into a rendering pipeline, or understanding of how ML components fit into graphics stacks.

Hands-on performance profiling and optimization of real workloads.

🟦Nice-to-Have Skills:

Ray tracing (DXR/VKRT), game engines (Unreal, Unity) or rendering middleware.

Color and image processing fundamentals (sRGB vs linear, HDR, resampling/filtering).

CUDA/HIP compute experience.

Render fidelity testing, SSIM/PSNR-based visual regression tooling.

CI-driven development, automated test harnesses.

🟦Languages:

English: upper intermediate