GPU Compute & MLIR Compiler Engineer

BuildxPartners; Bengaluru; On-site; INR 2,000,000 - 3,000,000; Full time

Job summary

BuildxPartners is seeking a GPU Compute & MLIR Compiler Engineer in Bengaluru, India. This role involves optimizing GPU compute kernels for AI/ML applications and developing MLIR-based compilation pipelines.

The ideal candidate will possess strong expertise in GPU programming, knowledge of MLIR frameworks, and proficiency in C/C++. A Bachelor's, Master's, or PhD in relevant fields is required along with at least 4 years of experience in the domain.

Qualifications

• 4+ years of relevant experience in compiler development, GPU programming, AI acceleration, or systems engineering.
• Strong expertise in developing MLIR frameworks and custom dialects.
• Experience with Vulkan Compute programming and GPU profiling.

Responsibilities

• Develop and optimize GPU compute kernels for AI/ML workloads using OpenCL and Vulkan Compute.
• Design, build, and extend MLIR dialects and compiler infrastructure.
• Collaborate with teams to optimize CV and LLM workloads.

Skills

GPU programming

MLIR compiler development

AI workload optimization

OpenCL kernel development

C/C++ programming

Education

Bachelor's/Master's/PhD in Computer Science, Electronics, Engineering, Information Systems

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 the Company

We are hiring on behalf of our client, a global technology leader known for developing cutting-edge semiconductor, AI, graphics, and compute solutions powering next-generation mobile, automotive, IoT, and intelligent computing platforms. The company is at the forefront of AI acceleration, GPU architecture, and high-performance computing innovations.

Role Overview

We are seeking a GPU Compute & MLIR Compiler Engineer with strong expertise in GPU programming, MLIR compiler development, and AI workload optimization. The role involves designing and optimizing GPU compute kernels, developing MLIR-based compilation pipelines, and driving performance improvements for AI/ML applications across modern GPU architectures.

Key Responsibilities

• Develop and optimize GPU compute kernels for AI/ML workloads using OpenCL and Vulkan Compute .
• Design, build, and extend MLIR dialects and compiler infrastructure across multiple abstraction levels.
• Develop compiler passes including tiling, fusion, vectorization, bufferization, and lowering pipelines .
• Optimize AI model execution through compiler-level and runtime-level performance enhancements.
• Perform GPU profiling, bottleneck analysis, and performance tuning using hardware counters and profiling tools.
• Build and maintain GPU runtime components, including memory management, resource scheduling, and execution pipelines.
• Develop efficient code generation pipelines from MLIR to GPU backends.
• Collaborate with ML, runtime, framework, and hardware teams to optimize CV and LLM workloads.
• Develop high-performance compiler and runtime components using modern C/C++ .

Required Skills

• Strong experience with MLIR framework .
• Hands-on experience creating custom dialects, compiler passes, and lowering pipelines.

Knowledge of:

• TOSA, StableHLO, ONNX-MLIR
• MemRef, SCF, GPU, LLVM Dialects
• Graph optimization and code generation techniques

GPU Programming

• Strong expertise in OpenCL kernel development and optimization.
• Experience with Vulkan Compute programming and runtime internals.
• Understanding of GPU architecture, memory hierarchy, parallel execution, and async compute.
• Experience profiling and optimizing GPU workloads.

Programming

• Strong proficiency in C/C++ .
• Experience developing system-level software and performance-critical components.

AI/ML

• Good understanding of AI/ML fundamentals.

Experience optimizing workloads related to:

• Computer Vision (CV)

Preferred Skills

• Experience with IREE , TVM , XLA , LLVM, or other MLIR-based deployment frameworks.
• Understanding of compiler/runtime architecture and hardware abstraction layers.
• Knowledge of quantization, mixed-precision inference, and model optimization.
• Exposure to multi-target compilation across CPU, GPU, and NPU.
• Familiarity with GPU profiling tools such as ARM Streamline, Intel VTune, Snapdragon Profiler, etc.
• Contributions to MLIR, LLVM, IREE, or related open-source projects.

Qualification

• Bachelor's/Master's/PhD in Computer Science, Electronics, Engineering, Information Systems, or related discipline.
• 4+ years of relevant experience in compiler development, GPU programming, AI acceleration, or systems engineering.