Note: The job is a remote job and is open to candidates in USA. EnCharge AI is seeking an AI Compiler Engineer to develop and optimize graph compilers for cutting-edge AI and ML workloads. The role collaborates with hardware architects and AI researchers to improve performance, optimize computation graphs, and enable efficient model deployment on EnCharge’s inference accelerators.

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

Skills

• Bachelor's or Master's degree in Computer Science, Electrical Engineering, or related field
• 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
• Ph.D. preferred
• Open-source contributions to AI software frameworks and libraries is a plus

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