Sr Staff Engineer AI/ML Compiler & Runtime Software Engineer AI SDK Team Location: Pune / Bangalore India Join the RISC-V Revolution!

About

GlobalFoundries GlobalFoundries is a leading full-service semiconductor foundry providing a unique combination of design, development, and fabrication services to some of the worlds most inspired technology companies. With a global manufacturing footprint spanning three continents, GlobalFoundries makes possible the technologies and systems that transform industries and give customers the power to shape their markets. For more information, visit www.gf.com Introduction We are seeking a highly skilled Sr Staff Engineer in AI/ML compiler and runtime software to join our Platform Software and AI SDK team.

The team is building the foundational software stack to enable Physical AI workloads on next-generation RISC-V IP and SoC platforms. This role sits at the intersection of AI compiler technology, edge AI deployment, runtime systems, and hardware acceleration. You will work on IREE-based compiler and runtime flows, LLVM/MLIR infrastructure, custom MLIR dialects and passes, code generation, quantization, and NPU acceleration to enable efficient execution of AI models on edge devices and silicon platforms.

This is a unique opportunity to contribute across the full silicon-to-software lifecycle, combining compiler engineering, AI runtime development, and hardware-software co-design to deliver high-performance, low-latency, and power-efficient AI execution for real-time edge and Physical AI use cases.

What Youll 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. Work with internal and customer-facing teams to support software enablement, debugging, performance tuning, and deployment of AI workloads on target platforms.

Ideally, youll have 3-12 years of hands-on software engineering experience, with robust experience in compiler, runtime, embedded software, or AI/ML systems. Strong hands-on experience with IREE, LLVM, and MLIR compiler infrastructure.

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 .

📌 AI/ML Compiler & Runtime Software Engineer (Pune)
🏢 Globalfoundries Engineering Private
📍 Pune