About the job
NVIDIA is looking for engineers for our core AI Frameworks (Megatron Core and NeMo Framework) team to design, develop and optimize diverse real world workloads. 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, reasoning, alignment, customization, evaluation, deployment and tooling to optimize performance and user experience.
Responsibilities
Develop algorithms for AI/DL, data analytics, machine learning, or scientific computing
Contribute and advance open source NeMo-RL, Megatron Core, NeMo Framework
Solve large-scale, end-to-end AI training and inference challenges, spanning the full model lifecycle from initial orchestration, data pre-processing, running of model training and tuning, to model deployment.
Work at the intersection of compter-architecture, libraries, frameworks, AI applications and the entire software stack.
Innovate and improve model architectures, distributed training algorithms, and model parallel paradigms.
Performance tuning and optimizations, model training and finetuning with mixed precision recipes on next-gen NVIDIA GPU architectures.
Research, prototype, and develop robust and scalable AI tools and pipelines.
Qualifications
Minimum
MS, PhD or equivalent experience in Computer Science, AI, Applied Math, or related fields.
5+ years of industry experience.
Experience with AI Frameworks (e.g. PyTorch, JAX, Ray), 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.
Preferred
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.
Prior experience with Reinforcement Learning algorithms and compute patterns
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.