GPU Compute, MLIR Engineer

EngineerEngineerFull TimeRemoteSeniorTeam 501-1,000Since 2002H1B No SponsorCompany SiteLinkedIn

Location

India

Posted

1 day ago

Salary

₹2,000K - ₹10,000K / year

Seniority

Senior

Bachelor Degree3 yrs expEnglish

Job Description

GPU Compute, MLIR Engineer

Weekday

• 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.

Job Requirements

  • 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.

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