About The Position

EnCharge AI is seeking a highly skilled and experienced AI Compiler Engineer to spearhead the efforts in developing and optimizing graph compilers tailored to cutting-edge AI and ML workloads. You will collaborate with hardware architects, and AI researchers to enhance performance, optimize computation graphs, and enable efficient model deployment on EnCharge’s Inference Accelerators.

Requirements

  • Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or related field (Ph.D. preferred).
  • 3+ years in compiler development, with a strong focus on AI or ML graph compilers.
  • Proficiency in AI graph compiler frameworks (e.g., MLIR, Torch-FX)
  • Solid background in hardware architectures (e.g., GPUs, TPUs, ASICs) and optimization techniques such as fusion, quantization, and tiling.
  • Familiarity with neural networks operators and code generation.
  • Strong understanding of intermediate representations, code parsing, and semantic analysis in compiler design.
  • Proficiency in C++, Python, or other programming languages commonly used in compiler development.
  • Demonstrated experience leading and mentoring engineering teams with successful project delivery.

Nice To Haves

  • Open-source contributions to AI software frameworks and libraries is a plus

Responsibilities

  • Architect, design, and implement optimizations for AI model execution on graph compilers to improve performance, reduce latency, and maximize hardware utilization.
  • Work closely with ML researchers, hardware engineers, and software developers to design and deploy AI models, understanding and addressing hardware-specific challenges.
  • Work on performance optimizations for neural network models, such as layer fusion, operator fusion, and graph-level transformations.
  • Develop compiler optimizations and passes that convert high-level AI models (e.g., from TensorFlow, PyTorch) into intermediate representations (IR).
  • Implement parsing, semantic analysis, and IR generation for deep learning frameworks.
  • Research and integrate the latest advancements in compiler design, ML model optimizations, and hardware acceleration into graph compilers.
  • Provide leadership, mentorship, and technical guidance to a team of engineers focused on graph compiler optimizations.
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