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# Principal AI Compiler Engineer

- **Company:** EnCharge AI, Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Artificial Neural Networks, C++ (Programming Language), Code Generation, Python (Programming Language), Machine Learning, Parsing, Tensorflow, Graphics Processing Unit (GPU), Pytorch, Deep Learning, Information Technology, Optimization Algorithms, Free and Open-Source Software, Hardware Acceleration, Programming Languages
- **Published:** September 22, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pm2ttgetd5

## About the Role

* Bachelor's or Master's degree in Computer Science, Electrical Engineering, or related field (Ph.D. preferred).
 * 10-15 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

## Description

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., * 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.