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AMD · 3 weeks ago

Neural Rendering Research Inference Engineer – Advanced Graphics Programs

Santa Clara, CA

Full-time

Hybrid

Mid, Senior Level

$191K/yr - $287K/yr

AMD is a leading company in next-generation computing experiences, focusing on innovation and collaboration. They are seeking a Neural Rendering Research Inference Engineer to improve the performance of machine learning applications through the design and optimization of GPU kernels for ML operators.

AI InfrastructureArtificial Intelligence (AI)Cloud ComputingComputerEmbedded SystemsGPUHardwareSemiconductor

Growth Opportunities

No H1B

Responsibilities

Work with ML researchers and engineers to translate neural network models and algorithms written in PyTorch/Onnx to efficient GPU shaders using languages such as HIP, Cuda, HLSL

Design, implement and optimize high performance GPU kernels for ML operators

Work across research, hardware, driver and compiler teams to analyze and troubleshoot performance issues, provide solutions to improve rendering speed and ML workload efficiency

Stay current with latest advancements in GPU hardware, rendering techniques, graphics APIs and GPU accelerated ML

Contribute to the design and development of tools and methodologies for optimized shader integration to game engines

Document and share knowledge on best practices for GPU programming (both graphics and compute/ML) within the team

Participate in code reviews and provide constructive feedback to peers

Qualification

C++HIPCUDAHLSLGPU optimizationPyTorchOnnxGPU architectureDirectXOpenGLVulkanEffective communicationProblem-solving skillsInterpersonal skills

Required

Deep technical expertise in translating neural network models and algorithms to efficient inference solutions

Strong programming expertise in C++, HIP, CUDA, HLSL and GPU-related technologies

Ability to effectively communicate and work optimally with different teams across AMD to create optimized inference implementations for neural rendering and generative ai applications

Passionate about improving the performance of key ML applications by designing, implementing and optimizing high performance GPU kernels for ML operators

Work with ML researchers and engineers to translate neural network models and algorithms written in PyTorch/Onnx to efficient GPU shaders using languages such as HIP, Cuda, HLSL

Design, implement and optimize high performance GPU kernels for ML operators

Work across research, hardware, driver and compiler teams to analyze and troubleshoot performance issues, provide solutions to improve rendering speed and ML workload efficiency

Stay current with latest advancements in GPU hardware, rendering techniques, graphics APIs and GPU accelerated ML

Contribute to the design and development of tools and methodologies for optimized shader integration to game engines

Document and share knowledge on best practices for GPU programming (both graphics and compute/ML) within the team

Participate in code reviews and provide constructive feedback to peers

Undergrad degree required

Preferred

Strong object-oriented programming background, C/C++ preferred

Proven experience in developing and optimizing GPU kernels for machine learning workloads (eg:, using HIP, OpenCL, CUDA, HLSL)

Ability to program in low level languages (x86 asm, SSE, ISA, Ptx, AMD assembly)

Strong understanding of GPU architecture (compute cores, cache hierarchy, memory model), graphics APIs (DirectX, OpenGL, Vulkan, etc.), and shader programming

Solid understanding of common neural network operators, their mathematical foundations, and computational requirements

Experience with modern concurrent programming and threading APIs

Experience with Windows, Linux operating system development

Experience with software development processes and tools such as debuggers, source code control systems (GitHub) and profilers is a plus

Understanding Machine Learning techniques and its application within graphics

Effective communication and problem-solving skills

Good interpersonal skills