✦ Luna Orbit — Software Engineering

GPU KERNEL OPTIMIZATION ENGINEER

at Advanced Micro Devices

📍 Austin, Texas, United States Hybrid Posted March 20, 2026
Type Full-Time
Experience mid
Exp. Years Not specified
Education Not specified
Category Software Engineering

This role focuses on developing, profiling, and optimizing GPU kernels used in machine learning applications, collaborating with teams to improve performance and efficiency.

  • Develop GPU kernels
  • Profile GPU performance
  • Optimize kernels
  • Collaborate with ML teams
  • Improve GPU libraries

Involves GPU kernel development, performance analysis, optimization techniques, and understanding GPU architecture within high-performance computing frameworks.

The ideal candidate is a skilled GPU optimization engineer with experience in developing and profiling GPU kernels, understanding GPU architecture, and working with machine learning libraries. They should be collaborative, detail-oriented, and eager to learn new optimization techniques.

developing GPU kernelsprofiling GPU performanceoptimizing GPU kernelscollaborating with ML teams
GPU architecture knowledgeGEMM algorithmshigh-performance computingopen-source software developmentgraph analysis
ROCmGPU profiling toolshigh-performance computing frameworksC++Python
GPU kernelsGPU architectureperformance profilingmatrix multiplicationML librariesoptimization techniques
GPU architectureGPU kernelsC++Pythonperformance profilingmatrix multiplicationML librariesoptimization techniques
collaborationindependent workproblem-solvingcontinuous learningcommunication
Industry Semiconductor / Hardware / Technology
Job Function GPU kernel development and optimization for machine learning
Role Subtype GPU Optimization Engineer
Tech Domains GPU Architecture, High-Performance Computing, Machine Learning
GPU kernelsGPU architectureperformance profilingmatrix multiplicationML librariesoptimization techniqueshigh-performance computingROCmC++Pythonopen-source softwaregraph analysisGPU performanceoptimizationprofiling

Lack of experience with GPU kernel development, No knowledge of GPU architecture, No experience with performance profiling, Absence of collaboration with ML teams

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