AI/ML Compiler & Runtime Software Engineer

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Skills you'll use

What you'll do

• Architect, design, and develop AI/ML compiler and runtime software for RISC-V based IP, NPU, and SoC platforms.
• Develop and enhance IREE-based compiler flows, including MLIR lowering, code generation, runtime integration, and deployment paths for edge AI workloads.
• Create and maintain custom MLIR dialects, compiler passes, lowering pipelines, and transformation flows to map AI workloads efficiently to custom NPU and accelerator hardware.
• Work across AI framework import paths including PyTorch, ONNX, and TFLite, and enable lowering through torch-mlir, TOSA, Linalg, and related MLIR dialects.
• Optimize neural network workloads for edge deployment, including operator fusion, tiling, memory planning, quantization, layout transformation, and accelerator-aware scheduling.
• Enable efficient execution of AI models across CPU, vector, matrix, and NPU acceleration paths, balancing latency, throughput, memory footprint, and power efficiency.
• Collaborate closely with architecture, hardware, firmware, FPGA, validation, and product teams to bring up AI workloads on simulators, FPGA platforms, emulation environments, and silicon.
• Analyze model performance, identify compiler/runtime bottlenecks, and drive optimizations across graph-level, operator-level, and kernel-level execution paths.
• Define software architecture and technical direction for AI SDK components, including compiler pipelines, runtime interfaces, model deployment flows, and accelerator integration.
• Build test infrastructure, validation flows, benchmark suites, and CI pipelines for AI compiler/runtime correctness, performance, and regression tracking.

Who can apply

• Experience developing MLIR dialects, compiler passes, lowering pipelines, pattern rewrites, code generation flows, or backend integration for custom hardware.
• Good understanding of IREE code generation flow, dispatch formation, executable generation, HAL/runtime concepts, and target-specific lowering.
• Strong exposure to AI compiler/runtime stacks used for edge AI or accelerator-backed inference.
• Experience with AI model formats and frameworks such as PyTorch, ONNX, TensorFlow Lite/TFLite, and related conversion or import flows.
• Working knowledge of torch-mlir, TOSA, Linalg, tensor dialects, bufferization, quantization dialects, and MLIR-based model lowering concepts.
• Strong understanding of neural network execution and optimization, including quantization, operator fusion, tensor layouts, memory planning, tiling, vectorization, and kernel selection.
• Experience enabling or optimizing workloads for AI accelerators, NPUs, DSPs, vector processors, matrix engines, or custom SoC IP.
• Strong C/C++ programming skills, with good Python scripting ability for compiler tooling, testing, automation, and model workflow integration.
• Experience working in Linux development environments, including cross-compilation, debugging, profiling, build systems, and runtime bring-up.
• Strong debugging and problem-solving skills across compiler IR, generated code, runtime behavior, and hardware/software interaction.

About GlobalFoundries

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