Senior Software Engineer, GPU
Team: Software
Location: Fremont, CA
Commitment: Regular, Full Time
Workplace Type: remote
Job Responsibilities
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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.
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Design and implement the GPU computing layer within our desktop software stack, introducing GPU acceleration for computationally intensive workloads.
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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.
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Port and optimize mesh processing algorithms and other performance-critical components from CPU implementations to GPU-accelerated implementations.
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Analyze performance bottlenecks and apply GPU optimization techniques such as memory layout optimization, kernel design, and efficient data transfer between CPU and GPU.
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Establish best practices, documentation, and architectural guidelines for GPU development to enable maintainable and scalable use of GPU acceleration across the codebase.
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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
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Strong experience developing GPU-accelerated software using frameworks such as CUDA, Kokkos, OpenCL, or similar technologies.
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Solid understanding of GPU architecture and parallel programming concepts, including memory hierarchies, kernel execution models, synchronization, and performance optimization.
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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.
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Experience integrating GPU tooling and compilers into modern build systems and development environments.
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Strong C++ programming skills and experience working in performance-sensitive codebases.
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Ability to translate CPU algorithms into efficient parallel GPU implementations.
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Strong problem-solving and performance-analysis skills, including profiling and debugging GPU code.
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