Principal Compiler Engineer - Parallel Computing Platforms

December 14, 2024

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Fabric Cryptography

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Fabric Cryptography is a company focused on revolutionizing the field of cryptography through the use of advanced hardware solutions. They have developed the Verifiable Processing Unit (VPU), a specialized chip that combines elements of GPUs and ASICs to accelerate cryptographic processes. The company's innovations lie in hardware-accelerated cryptography, allowing for high-performance systems where privacy is prioritized. Fabric's custom silicon chips, leveraging features such as finite-field arithmetic and optimized primitives, are designed to enhance the performance of various cryptographic algorithms. Additionally, Fabric offers a cloud solution to make advanced cryptographic operations accessible without the need for specialized hardware. The company's approach combines decades of experience in CPU, GPU, and hardware security module design to provide state-of-the-art cryptographic performance and flexibility.

11 - 50 employees

🔧 Hardware

🔒 Cybersecurity

📋 Description

• Fabric believes hardware determines the boundaries of humanity's collective creativity and imagination. • Building hardware for the next generation of cryptography to create a more trustworthy world. • Seeking a Compiler Engineer with expertise in code generation for massively-parallel computing architectures. • Contribute to the design and implementation of advanced compiler technologies. • Enable hardware-aware optimizations that unlock new levels of performance for custom hardware.

🎯 Requirements

• Advanced understanding of MLIR's multi-level intermediate representation. • Proven experience in designing and implementing custom dialects. • Expert-level skills in compiler development, including code generation techniques, optimization passes, and low-level transformations. • Fluency in systems programming languages such as C and C++. • Deep understanding of massively-parallel computing platforms, including GPUs, distributed models, and parallelization techniques. • Experience targeting hardware platforms like machine learning accelerators or custom AI hardware. • Experience creating custom computational languages tailored to high-performance computing. • Strong grasp of language design principles for specific domains. • Familiarity with TensorFlow XLA, PyTorch compilation technologies, or similar frameworks. • Experience optimizing computation graphs for machine learning.