AI Compiler Engineer

As an AI Compiler Engineer at EnCharge AI, you will play a crucial role in developing and optimizing graph compilers tailored to cutting-edge AI and ML workloads. Your collaboration with hardware architects and AI researchers will enhance performance, optimize computation graphs, and enable efficient model deployment on EnCharges 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. - Focus 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. Qualifications: - Bachelors or Masters degree in Computer Science, Electrical Engineering, or related field (Ph.D. preferred). - 3-12 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. - Open-source contributions to AI software frameworks and libraries are a plus. - Demonstrated experience leading and mentoring engineering teams with successful project delivery. If you are passionate about advancing AI hardware and software systems for edge-to-cloud computing and want to work with a team of veteran technologists, apply now to join EnCharge AI as an AI Compiler Engineer. As an AI Compiler Engineer at EnCharge AI, you will play a crucial role in developing and optimizing graph compilers tailored to cutting-edge AI and ML workloads. Your collaboration with hardware architects and AI researchers will enhance performance, optimize computation graphs, and enable efficient model deployment on EnCharges 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. - Focus 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. Qualifications: - Bachelors or Masters degree in Computer Science, Electrical Engineering, or related field (Ph.D. preferred). - 3-12 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. - Open-source contributions to AI software frameworks and libraries are a plus. - Demonstrated experience leading and mentoring engineering teams with successful project delivery. If you are passionate about advancing AI hardware and software systems for edge-to-cloud computing and want to work with a team of veteran technologists, apply now to join EnCharge AI as an AI Compiler Engineer.