英伟达Senior LLM Train Framework Engineer
任职要求
• MS, PhD or equivalent experience in Computer Science, AI, Applied Math, or related fields and 5+ years of industry experience. • Experience with AI train frameworks (e.g., PyTorch, JAX), and/or inference and deployment environments (e.g., TRTLLM, vLLM, SGLang). • Proficiency in decentralized instruction. • Proficient in Python programming, software development, debugging, performance analysis, test composition, and documentation. • CUDA or collective programming skills are a big plus. • Consistent record of working effectively across multiple engineering initiatives and improving AI libraries with new innovations. • S…
工作职责
NVIDIA is now looking for LLM Train Framework Engineers for the Megatron Core team. Megatron Core is open-source, scalable, and cloud-native frameworks built for researchers and developers working on Large Language Models (LLM) and Multimodal (MM) foundation model pretraining and post-training. Our GenAI Frameworks provide end-to-end model training, including pretraining, alignment, customization, evaluation, deployment, and tooling to optimize performance and user experience. Build on Megatron Core Framework's capabilities by inventing advanced distributed training algorithms and model optimizations. Collaborate with partners to implement optimized solutions. What you’ll be doing: • Build and develop open source Megatron Core. • Address extensive AI training and inference obstacles, covering the entire model lifecycle including orchestration, data pre-processing, conducting model training and tuning, and deploying models. • Work at the intersection of AI applications, libraries, frameworks, and the entire software stack. • Spearhead advancements in model architectures, distributed training strategies, and model parallel approaches. • Enhance the pace of foundation model training and optimization through mixed precision formulas and advanced NVIDIA GPU structures. • Performance tuning and optimizations of deep learning framework and software components. • Research, prototype, and develop robust and scalable AI tools and pipelines.
Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply Generative AI algorithms to solve real world problems with significant impact? The Generative AI Innovation Center helps AWS customers implement Generative AI solutions and realize transformational business opportunities. This is a team of strategists, scientists, engineers, and architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI. Starting in 2024, the Innovation Center launched a new Custom Model and Optimization program to help customers develop and scale highly customized generative AI solutions. The team helps customers imagine and scope bespoke use cases that will create the greatest value for their businesses, define paths to navigate technical or business challenges, develop and optimize models to power their solutions, and make plans for launching solutions at scale. The GenAI Innovation Center team provides guidance on best practices for applying generative AI responsibly and cost efficiently. You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. We’re looking for Applied Scientists capable of using GenAI and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems. As an Applied Scientist, you will - Collaborate with AI/ML scientists and architects to research, design, develop, and evaluate generative AI solutions to address real-world challenges - Interact with customers directly to understand their business problems, aid them in implementation of generative AI solutions, brief customers and guide them on adoption patterns and paths to production - Help customers optimize their solutions through approaches such as model selection, training or tuning, right-sizing, distillation, and hardware optimization - Provide customer and market feedback to product and engineering teams to help define product direction