About the position
NVIDIA is now looking for AI Software Engineers for our GenAI Frameworks (Megatron Core and NeMo Framework) team. Megatron Core and NeMo Framework are 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. In this critical role, you will expand Megatron Core and NeMo Framework's capabilities, enabling users to develop, train, and optimize models by designing and implementing the latest in distributed training algorithms, model parallel paradigms, model optimizations, defining robust APIs, meticulously analyzing and tuning performance, and expanding our toolkits and libraries to be more comprehensive and coherent. You will collaborate with internal partners, users, and members of the open source community to analyze, design, and implement highly optimized solutions.
Responsibilities
- Design and develop the GenAI open source Megatron Core and NeMo Framework
- Solve large-scale, end-to-end AI training and inference challenges, spanning the full model lifecycle from initial orchestration, data pre-processing, and running of model training and tuning, to model deployment.
- Work at the intersection of AI applications, libraries, frameworks, and the entire software stack.
- Innovate and improve model architectures, distributed training algorithms, and model parallel paradigms.
- Accelerate foundation model training and finetuning with mixed precision recipes and next-gen NVIDIA GPU architectures.
- Performance tuning and optimizations of deep learning framework and software components.
- Research, prototype, and develop robust and scalable AI tools and pipelines.
Requirements
- MS, PhD or equivalent experience in Computer Science, AI, Applied Math, or related fields and 5+ years of industry experience.
- Experience with AI Frameworks (e.g. PyTorch, JAX), and/or inference and deployment environments (e.g. TRTLLM, vLLM, SGLang).
- Proficient in Python programming, software design, debugging, performance analysis, test design and documentation.
- Consistent record of working effectively across multiple engineering initiatives and improving AI libraries with new innovations.
- Strong understanding of AI/Deep-Learning fundamentals and their practical applications.
Nice-to-haves
- Hands-on experience in large-scale AI training, with a deep understanding of core compute system concepts (such as latency/throughput bottlenecks, pipelining, and multiprocessing) and demonstrated excellence in related performance analysis and tuning.
- Expertise in distributed computing, model parallelism, and mixed precision training.
- Prior experience with Generative AI techniques applied to LLM and Multi-Modal learning (Text, Image, and Video).
- Knowledge of GPU/CPU architecture and related numerical software.
- Contributions to open source deep learning frameworks.
Benefits
- Equity and benefits
