Backend Inference Runtime Engineer Graduate (AML Inference) - 2027 Start

Minimum Qualifications: - Individuals who are completing or have recently completed a Bachelor's/ Master's degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline, or a related discipline. - Solid foundation in computer low-level knowledge, proficient in C/C++ and Python programming, skilled in CUDA programming and familiar with GPU hardware architecture principles, and well-versed in GPU memory models, computing scheduling, and communication mechanisms; - Proficiently master the underlying development and implementation of various basic operators in Deep learning, be well-versed in GPU adaptation and optimization of core operators such as matrix operations, normalization, and activation functions, and be able to independently complete operator handwritten reconstruction, memory access optimization, vectorization acceleration, and precision alignment to ensure high performance and high stability of operator inference. - Familiar with the end-to-end process of deep learning inference compilation, understand core compilation technologies such as computational graph optimization, operator fusion, constant folding, memory reuse, scheduling optimization, and quantization compilation, and be able to simplify the inference process, reduce GPU memory usage, and decrease inference latency through compilation-level improvements, thereby significantly enhancing the throughput efficiency of model inference. - Proficient in using GPU performance analysis tools such as Nsight and Profiler, able to accurately identify performance bottlenecks such as computing power waste, memory access blockage, and scheduling redundancy during the inference process, possess the thinking of software-hardware collaborative optimization, capable of outputting systematic optimization solutions and completing implementation iterations, and adaptable to the requirements of industrial-level high-concurrency, low-latency inference business. - Possess good cross-team collaboration skills, communication and presentation skills, and document writing skills, have strong sense of responsibility and stress tolerance, and be able to drive the resolution of complex technical issues and the implementation of projects;

Preferred Qualifications: - Thoroughly understand the core principles of large model inference, proficiently master the core technologies of model parallelism, have experience in implementing distributed inference solutions such as tensor parallelism, pipeline parallelism, and sequence parallelism, and be familiar with multi-card communication, load balance, and parallel efficiency optimization methods. - Those with experience in secondary development and Performance optimization of mainstream large model inference frameworks such as vLLM, SGLang, TensorRT-LLM, etc. are preferred.