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).

Responsibilities:

🌸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.

Skills:

💦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).

Nice to have:

💦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.

Apply

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