EnCharge AI - AI Compiler Engineer
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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. • Open-source contributions to AI software frameworks and libraries is a plus • Demonstrated experience leading and mentoring engineering teams with successful project delivery. • EnchargeAI is an equal employment opportunity employer in the United States.The salary range for this position is $190,000 to $255,000 USD per year. (Per Year: $195,000 to $265,000 CAD | €110,000 to €160,000 EUR | 1,206,458 to 1,754,848 NOK)Actual compensation offered will be determined based on job-related knowledge, skills, and experience.
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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