Senior Software Engineer, GPU

Job Responsibilities

• 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. 

Requirements

• 5-8 years of experience

• Strong experience developing GPU-accelerated software using frameworks such as CUDA, Kokkos, OpenCL, or similar technologies. 

• Solid understanding of GPU architecture and parallel programming concepts, including memory hierarchies, kernel execution models, synchronization, and performance optimization. 

• Experience evaluating and comparing different GPU programming models and frameworks, and making informed technical decisions about trade-offs such as performance, portability, and developer productivity. 

• Experience integrating GPU tooling and compilers into modern build systems and development environments. 

• Strong C++ programming skills and experience working in performance-sensitive codebases. 

• Ability to translate CPU algorithms into efficient parallel GPU implementations. 

• Strong problem-solving and performance-analysis skills, including profiling and debugging GPU code.