AI/ML Compiler & Runtime Software Engineer

About this role

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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.
• Provide technical leadership to engineers working on AI compiler, runtime, model deployment, and edge AI software development.

Who can apply

• Familiarity with RISC-V Vector, matrix acceleration concepts, custom instructions, or accelerator-specific code generation.
• Experience with edge AI deployment on real devices, development boards, FPGA platforms, emulators, simulators, or early silicon.
• Familiarity with FPGA prototyping, Linux bring-up, board-level debugging, or pre-silicon software validation.
• Exposure to LLM and edge inference stacks such as llama.cpp, GGML/GGUF, ONNX Runtime, TensorFlow Lite, TVM, XNNPACK, or similar frameworks, with understanding of quantization, memory footprint optimization, kernel performance, and deployment constraints on resource-limited devices.
• Experience with AI model benchmarking and optimization for vision, audio, transformers, GenAI, robotics, automotive, industrial, or real-time embedded workloads.
• Understanding of hardware-software co-design, memory hierarchy, DMA, scratchpad memory, cache behavior, and accelerator data movement. Experience with runtime systems, kernel libraries, microkernels, custom dispatch flows, or accelerator runtime APIs.
• Familiarity with CI/CD and agile tools such as Jenkins, Git, CMake, Bazel, Jira, or similar engineering infrastructure.
• Experience working in customer-facing enablement, silicon bring-up, platform software, or SDK delivery environments.
• Excellent communication and interpersonal skills, with the ability to explain complex compiler and AI runtime topics clearly to software, hardware, and product stakeholders.
• GlobalFoundries is an equal opportunity employer, cultivating a diverse and inclusive workforce. We believe having a multicultural workplace enhances productivity, efficiency and innovation whilst our employees feel truly respected, valued and heard.

About GlobalFoundries

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