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
Strong hands-on experience with MLIR framework, including authoring and extending custom dialects, writing compiler passes, and building end-to-end lowering pipelines.
Deep expertise across MLIR abstraction levels
Frontend dialects — ingestion and representation of ML models (e.g., TOSA, StableHLO, ONNX-MLIR)
Graph-level IR — high-level operation fusion, shape inference, and graph transformations
Tensor IR level — structured op representation using Linalg, Tensor, and Vector dialects; tiling and fusion strategies
Runtime/low-level dialects — bufferization, MemRef, SCF, GPU, and LLVM dialects for final code generation
Strong hands-on experience in OpenCL programming, including kernel development, memory model, work-group/work-item optimization, and OpenCL runtime management.
Solid understanding of Vulkan compute programming, including descriptor management, compute pipelines, synchronization primitives, and Vulkan runtime internals.
Strong understanding of GPU architecture, memory hierarchies, and async compute.
Proficiency in C/C++ for system-level development.
Experience with kernel profiling and bottleneck analysis on GPU platforms.
Strong background in machine learning fundamentals, covering both CV and LLM workloads.
Good to Have
Hands-on experience with IREE (Intermediate Representation Execution Environment) or other MLIR-based deployment frameworks such as TVM, XLA, or LLVM.
Familiarity with IREE's compiler and runtime architecture — including HAL (Hardware Abstraction Layer), executable compilation, and dispatch mechanisms — particularly its OpenCL and Vulkan HAL backends.
Experience contributing to open-source MLIR or IREE projects.
Knowledge of quantization, mixed-precision inference, and model optimization techniques for edge and server GPU targets.
Exposure to multi-target compilation (CPU, GPU, NPU) using MLIR-based toolchains.
Familiarity with cross-vendor GPU profiling tools (e.g., ARM Streamline, Qualcomm Snapdragon Profiler, Intel VTune) for OpenCL and Vulkan workloads.
Minimum Qualifications
- Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 4+ years of Systems Engineering or related work experience.
- OR
- Master's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Systems Engineering or related work experience.
- OR
- PhD in Engineering, Information Systems, Computer Science, or related field and 2+ years of Systems Engineering or related work experience.
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