GPU Software Engineer (Graphics / ML)

We are looking for hybrid engineers - real-time graphics AND machine learning in one person - to join a GPU software team working at the intersection of rendering and image ML (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. Pure graphics engineers with no ML project experience, and ML engineers with no GPU/graphics programming experience, are not a fit for this role.

Develop and optimize rendering and 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.

4+ years of professional software engineering experience (C++ primary; strong Python on the ML side).

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

BOTH areas below are mandatory - a profile covering only one of them is not a match:

(a) GPU / graphics programming: hands-on production experience with DX12 and/or Vulkan (or CUDA/HIP GPU compute), shader/kernel authoring (HLSL/GLSL/compute), GPU debugging and profiling (RenderDoc, PIX, Radeon GPU Profiler, Nsight).

(b) ML on images: hands-on project experience with PyTorch (or equivalent) on vision models - super-resolution, denoising, artifact suppression, segmentation or comparable CNN/transformer work - including inference deployment and optimization on GPU (ONNX Runtime or TensorRT, quantization).

Depth may be asymmetric - deep expertise in one area plus solid practical project experience in the other is acceptable - but real project experience in BOTH is required. Graphics-only profiles with no ML project work, and ML profiles with no GPU/graphics programming (tabular / NLP / LLM-only data science), do not qualify.

Hands-on performance profiling and optimization of real workloads.

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.