Job Description

Job Description
We are looking for an engineer to develop solutions at the intersection of real-time graphics and machine learning. The selected person will participate in the development and optimization of graphics pipelines, the integration of artificial intelligence models and the improvement of the performance of interactive visual applications, collaborating with teams specialized in graphics, platforms and drivers.
Responsibilities:
Develop and optimize rendering and inference components of machine learning models for real-time graphic applications.
Integrate artificial intelligence models, such as super-resolution, denoising, and artifact suppression, into rendering pipelines.
Optimize the performance of GPU workloads, improving latency, memory usage and overall performance.
Analyze and profile applications using debugging and performance analysis tools for GPUs.
Evaluate the visual quality of the results using objective and perceptual metrics, as well as visual regression tools.
Develop clean, maintainable and easily testable code, following good engineering practices.
Collaborate with graphics, machine learning, and platform teams to design and implement high-performance solutions.
Requirements:
More than 4 years of experience in software development with C++. For profiles specialized in machine learning, proficiency in Python and practical knowledge of C++ are required.
Knowledge of GPU architecture and operation, including graphics pipelines, synchronization, memory management and performance optimization.
Advanced experience in at least one of the following areas:
Real-time graphics, using DirectX 12, Vulkan, shader development (HLSL or GLSL), rendering techniques and debugging and analysis tools such as RenderDoc, PIX or Radeon GPU Profiler.
Machine Learning applied to images, using PyTorch, super-resolution models, noise removal or artifact suppression, as well as deployment and optimization of inferences on GPUs using ONNX Runtime, TensorRT or quantization techniques.
Functional knowledge of the complementary area, with the ability to integrate AI models into graphics pipelines or understand the integration of machine learning components in rendering systems.
Hands-on experience analyzing, profiling and optimizing high-performance applications in real-world environments.

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