# GPU Compute & MLIR Engineer

> Weekday AI · India (Remote) · Full-time · Posted 2026-07-01

**Salary:** INR 2,000,000–10,000,000

**Workplace:** remote

**Department:** Weekday's Client via platform

## Description

**This role is for one of Weekday’s clients  
Salary range: Rs 2000000 - Rs 10000000 (ie INR 20 - 100 LPA)**

  
Min Experience: 3+ years  
Location: India  
JobType: full-time

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.

## Requirements

**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.

### Must-have skills

gpu computing, MLIR, C/C++

### Good-to-have skills

vulkan, Kernel, OpenCL

## Apply

[Apply at Weekday AI](https://apply.workable.com/weekday-1/j/7DE47340CC/apply)

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