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Charterhouse is currently working with a rapidly growing technology innovator at the forefront of AI hardware solutions. Our client specializes in designing and developing cutting-edge, customized server hardware optimized for artificial intelligence and machine learning applications. Their mission is to accelerate AI initiatives globally by delivering high-performance, scalable, and energy-efficient infrastructure. As part of their expansion, they are looking to hire an experienced Compiler Engineer to join their team. This is an opportunity to work in a dynamic environment where contributions will directly influence next-generation AI hardware and software ecosystems.
About the Role
In this role, the Compiler Engineer will design and implement MLIR dialects and lowering pipelines targeting advanced AI accelerator/NPU platforms. The role includes transforming ML graphs from frameworks such as ONNX and PyTorch into optimized kernels with a focus on fusion, tiling, and vectorization. A critical part of the work will involve profiling, benchmarking, and optimizing compiler output to meet stringent latency, throughput, and power targets. Collaboration with hardware architects and runtime/framework teams will be essential to co-design compiler features. Additionally, you will build a portable compiler stack that supports multiple hardware generations.
About You
Our client is looking to hire a qualified Engineer with at least five years’ experience working in compiler engineering or high-performance systems. The candidate should be confident in modern C++ programming and comfortable using Python for scripting and tooling. Hands-on experience with MLIR or LLVM is key, especially in areas like dialect design, lowering, and optimization. A strong understanding of compiler internals such as intermediate representations, scheduling, vectorization, and loop transformations. The successful candidate also needs familiarity with major AI/ML frameworks such as PyTorch, ONNX, and TensorFlow, along with a good grasp of core machine learning operations like tensor manipulation, matrix multiplications, quantization, and handling dynamic shapes. A degree in Computer Science, Computer Engineering, or a related field is required; an advanced degree (M.S. or Ph.D.) is preferred.
A candidate with experience targeting NPU or AI accelerator hardware will be highly valued. Contributions to open-source compiler projects such as MLIR, LLVM, Torch-MLIR, or ONNX-MLIR are considered a strong advantage. Familiarity with runtime systems, scheduling, resource sharing, and memory movement engines will be beneficial.
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