英伟达Developer Technology Engineer - AI
任职要求
• A degree or equivalent experience from a university in an engineering or computer science related field. A masters or doctoral degree is preferred. • 2+ years of work experience. • Solid understanding of C, C++, Python, or Fortran. • Strong knowledge of software development, programming techniques, and algorithms. • Strong mathematical fundamentals, including linear algebra and numerical methods. • Background in parallel programming and accelerated computing, with comprehensive knowledge of parallel architectures and methods for performance analysis and tuning. Experience in GPU programming is desirable. • Experience in full-stack performance analysis and optimization within at least one of these areas: large language models and high-performance compu…
工作职责
• Working directly with key application developers to understand the current and future problems they are solving. You will build and optimize core parallel algorithms and data structures to deliver the most effective solutions using GPUs, through both library development and direct contribution to applications. This includes training and inference optimization for large language models (LLM), contributing to frameworks and open-source projects in the large language models ecosystem, such as Megatron and TRTLLM, SGLang, vLLM... • Collaborating closely with the architecture, research, libraries, tools, and system software teams at NVIDIA to influence the build of next-generation architectures, software platforms, and programming models. This includes investigating impact on application performance and developer efficiency, and turning real-world developer feedback into actionable platform improvements. • Engaging in deep optimization of high-performance operators, involving but not limited to GPU kernel optimization, instruction-level tuning, and compiler optimization. These optimizations will directly support customers or be coordinated within computation libraries and open-source projects across the community, like cuDNN, cuBLAS, and CUTLASS and Open- source libs like DeepGEMM, FlashMLA, FlashAttention, Flashinfer... • Improving communication for broad distributed large language models workloads. You will spearhead advancements in distributed training and inference by refining communication libraries(NCCL，NCCL GIN , NVSHMEM) and engaging in open-source communication libraries(like DeepEP, NCCL EP). This demands in-depth study of interconnect topologies(NVLINK) and network protocols(InfiniBand/RoCE) to design efficient data transfer strategies and methods for compute-communication overlap.
• Working directly with key application developers (especially LLM) to understand the current and future problems they are solving, creating and optimizing core parallel algorithms and data structures to provide the best solutions using GPUs, through both library development and direct contribution to the applications. This includes training and inference optimization for large language models, directly contributing to frameworks such as Megatron and TRTLLM, SGLang, vLLM... • Collaborating closely with the architecture, research, libraries, tools, and system software teams at NVIDIA to influence the design of next-generation architectures, software platforms, and programming models, including by investigating impact on application performance and developer productivity. • Engaging in deep optimization of high-performance operators, involving but not limited to CUDA deep optimization, instruction and compiler optimization. These optimizations will directly support customers or be integrated into products like cuDNN, cuBLAS, and CUTLASS... • Some travel is required for conferences and for on-site visits with developers.
• Research and develop cutting-edge techniques in deep learning, machine learning, HPC (High Performance Computing), graphs and data analytics, and perform in-depth analysis and optimization to ensure the best performance on NVIDIA current- and next-generation accelerated computing platform, including GPU, CPU and DPU. • Work directly with key customers to understand the current and future problems they are solving and optimize their workloads to maximize performance on our platform. • Collaborate closely with the architecture, research, libraries, tools, and system software teams at NVIDIA to design and develop next-generation architectures, software platforms, and programming models.
• Working directly with key application developers (especially LLM) to understand the current and future problems they are solving, creating and optimizing core parallel algorithms and data structures to provide the best solutions using GPUs, through both library development and direct contribution to the applications. This includes training and inference optimization for large language models, directly contributing to frameworks such as Megatron and TRTLLM, SGLang, vLLM... • Collaborating closely with the architecture, research, libraries, tools, and system software teams at NVIDIA to influence the design of next-generation architectures, software platforms, and programming models, including by investigating impact on application performance and developer productivity. • Engaging in deep optimization of high-performance operators, involving but not limited to CUDA deep optimization, instruction and compiler optimization. These optimizations will directly support customers or be integrated into products like cuDNN, cuBLAS, and CUTLASS... • Some travel is required for conferences and for on-site visits with developers.