About Our Client

Our client is a global technology leader developing next-generation semiconductor and AI computing platforms. Their engineering teams build high-performance hardware and software solutions powering mobile, automotive, AI, and edge computing products used worldwide.

Role Summary

We are looking for a highly skilled GPU Compute, MLIR Compiler, and Kernel Optimization Engineer with deep expertise in GPU compute, MLIR-based code generation, and end-to-end performance optimization for AI workloads. In this role, you will design, optimize, and deploy high-performance GPU compute kernels, build and extend MLIR compiler backends, and collaborate closely with ML, runtime, and hardware teams to push the limits of performance on up-to-date GPU architectures.

Key Responsibilities

- 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—including 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 the 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 operation 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 asynchronous 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 Computer Vision (CV) and Large Language Model (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 compiler and runtime architecture, including HAL (Hardware Abstraction Layer), executable compilation, and dispatch mechanisms, particularly 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 such as ARM Streamline, Snapdragon Profiler, and Intel VTune for OpenCL and Vulkan workloads.

Minimum Qualifications

- Bachelor's degree in Engineering, Information Systems, Computer Science, or a related field with 4+ years of Systems Engineering or relevant industry experience.

OR

- Master's degree in Engineering, Information Systems, Computer Science, or a related field with 3+ years of Systems Engineering or relevant industry experience.

OR

- PhD in Engineering, Information Systems, Computer Science, or a related field with 2+ years of Systems Engineering or relevant industry experience.

📌 GPU Compute & MLIR Compiler Engineer (Bengaluru)
🏢 BuildxPartners
📍 Bengaluru