Description

Drive AI ecosystem strategy and performance optimization for Windows RTX platforms through deep systems engineering.

Responsibilities

• Partner with software, research, and architecture teams to align AI strategies for graphics, browsers, and edge devices.
• Perform end-to-end optimization of AI models, data pipelines, and inference runtimes on GPU architectures.
• Implement compute and memory optimizations including quantization, pruning, and distillation for efficient edge deployment.
• Enhance inference pipelines using ONNX Runtime, DirectML, PyTorch, and TensorRT.
• Profile, debug, and optimize high-performance computing workloads and GPU-accelerated libraries.

Required Skills

• 5+ years of experience in AI/ML systems, inference pipelines, or GPU optimization.
• Strong C++ programming skills with a solid understanding of data structures and algorithms.
• Hands-on experience with ML/DL frameworks such as ONNX Runtime, PyTorch, TensorRT, or DirectML.
• Experience optimizing AI models for performance, scalability, and memory efficiency.
• Exposure to GPU programming and high-performance computing (CUDA preferred).
• Experience with model optimization techniques like quantization, pruning, and distillation.
• B.E. or B.Tech degree.

Preferred Skills

• Familiarity with APIs such as DirectX, Vulkan, or similar.

Key Skills

Education

Any Graduate