Esperanto delivers high-performance, energy-efficient, and innovative computing solutions that are the compelling choice for the most demanding AI and non-AI applications. The changing, computationally intensive workloads of the machine learning era mandate a new clean-sheet solution, without the baggage of existing legacy architectures, or the programmability limitations of overspecialized hardware. Esperanto leverages the simple, elegant, open standard RISC-V ISA along with leading-edge system architectures to deliver flexibility, scalability, performance, and energy-efficiency advantages.
We're seeking a talented AI Compiler Engineer to join our team and play a vital role in developing optimizations for the next-generation AI solutions. You'll work alongside a team of passionate engineers, applying your expertise to optimize and accelerate AI software.
Responsibilities Possess a strong understanding of cache hierarchy, memory access patterns, and their impact on AI model performance. Leverage your expertise in assembly language and low-level C++ to write highly optimized code for massively-parallel architectures. Demonstrate a solid grasp of CPU and accelerator microarchitecture concepts to effectively exploit architectural features for AI workloads. Employ advanced profiling techniques and optimization strategies to continuously improve the performance of AI software. Combine a strong understanding of linear algebra and algorithmic proficiency to efficiently implement matrix multiplication algorithms within ML frameworks. Exhibit exceptional skill in optimizing AI models for diverse hardware platforms, including advanced techniques like quantization and model drafting. Collaborate with the AI Software leadership to define and implement a long-term strategic vision for the AI software stack. Possess a deep understanding of kernel optimization techniques, employing methods such as weight prefetching and vectorization to minimize execution cycles and accelerate computation. Advocate for maximizing the efficiency of AI algorithms and hardware utilization through a data-driven approach. 5+ years of job related experience. Additional Qualifications Experience with industry-leading AI frameworks (e.g., ONNXRuntime, PyTorch) is a plus. Familiarity with parallelization techniques (e.g., OpenMP, MPI) would be beneficial.
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