GPU Compute & MLIR Compiler Engineer

BuildxPartners; Bengaluru Urban; On-site; INR 2,500,000 - 5,000,000; Full time

Job summary

BuildxPartners is seeking a GPU Compute, MLIR Compiler, and Kernel Optimization Engineer to design, optimize, and deploy high-performance GPU kernels for AI workloads. You will extend MLIR-based backends, work across frontend, graph-level IR, and tensor IR, and collaborate with ML, runtime, and hardware teams to push performance on modern GPUs.

The role requires deep expertise in MLIR, OpenCL, and Vulkan, strong C/C++ skills, and hands-on experience profiling and tuning GPU code for

Qualifications

• Strong hands-on experience with the MLIR framework, including custom dialects and compiler passes.
• Deep expertise in MLIR abstraction levels and end-to-end lowering pipelines.
• Proficiency in OpenCL and Vulkan backends for high-throughput AI workloads.
• Strong C/C++ skills for system-level development and kernel optimization.

Responsibilities

• Design, implement, and optimize MLIR-based compiler backends across frontend to runtime dialects.
• Develop GPU compute kernels for OpenCL and Vulkan, focusing on AI workloads.
• Profile, analyze bottlenecks, and apply compiler optimizations to improve throughput.
• Collaborate with ML, runtime, and hardware teams to optimize model lowering.
• Build GPU runtime infrastructure and ensure efficient memory management and scheduling.

Skills

MLIR

OpenCL

Vulkan

C/C++

GPU architecture

Kernel optimization

Profiling

Education

Bachelor's degree in Engineering/CS/IS

Master's degree in Engineering/CS

PhD in related field

Tools

MLIR toolchain

IREE / MLIR stacks

OpenCL

Vulkan API

Job description

Bangalore North, India | Posted on 18/06/2026

BuildxPartners is a global talent solutions firm delivering end-to-end recruitment and workforce solutions across industries and geographies.

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Job Description

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 modern 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.