• Evaluate and select the appropriate GPU computing technologies and frameworks (e.g., CUDA, Kokkos, or other modern GPU programming models) based on performance, portability, maintainability, and long-term architectural goals. • Design and implement the GPU computing layer within our desktop software stack, introducing GPU acceleration for computationally intensive workloads. • Integrate GPU development into the existing build and tooling ecosystem, including configuring the build system, dependency management, CI/CD workflows, and developer tooling to support GPU targets. • Port and optimize mesh processing algorithms and other performance-critical components from CPU implementations to GPU-accelerated implementations. • Analyze performance bottlenecks and apply GPU optimization techniques such as memory layout optimization, kernel design, and efficient data transfer between CPU and GPU. • Establish best practices, documentation, and architectural guidelines for GPU development to enable maintainable and scalable use of GPU acceleration across the codebase. • Collaborate with other engineers to identify additional opportunities for GPU acceleration and ensure seamless integration with the broader application architecture.