GPU Compute & MLIR Compiler Engineer — AI Workloads

About this role

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Skills you'll use

What you'll do

• Develop and optimize GPU compute kernels targeting OpenCL and Vulkan compute backends for high-throughput AI/ML workloads.
• Design, build, and extend MLIR dialects across multiple abstraction levels — including frontend dialects, graph-level IR, tensor IR (e.g., Linalg, Tensor, Tosa), and runtime/low-level dialects — to enable efficient end-to-end model compilation.
• Implement and maintain MLIR-based compiler passes and transformations, including tiling, fusion, bufferization, vectorization, and lowering pipelines targeting OpenCL and Vulkan GPU backends.
• Conduct profiling and bottleneck analysis of compiled kernels using GPU counters and vendor-specific profilers, and drive performance improvements through compiler-level optimizations.
• Build and maintain GPU runtime infrastructure for both OpenCL and Vulkan, including memory management, pipeline setup, command buffer orchestration, and resource scheduling.
• Develop and extend code generation pipelines, enabling automatic lowering from tensor IR through MLIR to efficient OpenCL and Vulkan GPU kernels.
• Implement performance-critical schedules — tiling, loop fusion, parallelism, and caching strategies — within MLIR-based backends targeting OpenCL and Vulkan runtimes.
• Collaborate with framework teams to optimize end-to-end model lowering for computer vision and LLM workloads using MLIR compilation stacks.
• Design and implement robust compiler and runtime components using modern C/C++, leveraging advanced programming paradigms for high-performance systems.
• Required Qualifications

Who can apply

• Strong hands-on experience with MLIR framework, including authoring and extending custom dialects, writing compiler passes, and building end-to-end lowering pipelines.

About Qualcomm

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