ディープラーニングソフトウェアエンジニア

最新のDNNアルゴリズムを分析、プロファイリング、最適化し、次世代ハードウェア上の遅延に敏感なユースケースのための高品質な本番ソフトウェアライブラリを実装します。モデル圧縮、量子化、アーキテクチャ探索技術を通じて、DNNのパフォーマンスと効率性の最先端を押し広げます。NVIDIAの研究者やエンジニアと協力し、将来のNVIDIAチップのアーキテクチャを改善し、最新のAI技術をサポートできるよう準備します。さらに、AIテクノロジーを基盤とした革新的な製品を顧客が実現できるよう支援し、コンピュータビジョン、音声認識、自然言語処理、最適化、生成AIなど、さまざまな領域でDNNモデルを作成し、実際の製品開発における技術的な課題を解決します。

・University degree, or equivalent knowledge, in Computer Science, Electrical Engineering, Physics or Mathematics ・5+ years of work experience in related fields, such as HPC, numeric computing, machine learning, AI with responsibilities for software optimization ・Proficiency in C++, Python, data structures, algorithms, computer architecture and operating system concepts ・Knowledge of deep-learning toolchains (PyTorch, TensorFlow, Keras, ONNX, TensorRT, numeric libraries, containers, etc.) ・Experience with neural network training, pruning and quantization, deploying DNN inference in production systems ・Experience optimizing and implementing compute algorithms on accelerated hardware, such as SIMD instruction sets, GPU-s, FPGA or DNN ASIC ・Familiarity with CNN, LLM and ViT architectures, as well as the latest progress in the field ・Experience creating DNN models for solving production problems in any domain, including computer vision, speech recognition, natural language processing, optimization or generative AI

・Experience implementing DNN inference natively using C++, CUDA kernels or low level libraries, such as BLAS ・Experience building distributed deep-learning infrastructure, HPC, cloud programming ・Contribution to open-source projects, including personal projects published as open-source ・Published paper at relevant conferences or in journals (e.g. NIPS, ICML, ICLR, CVPR, ICCV, ECCV, SIGGRAPH, etc.) ・Achievements in programming or machine learning competitions, such as Kaggle, HackerRank, TopCoder, etc.