Senior Research Engineer

Join NVIDIA and help build the software that will define the future of generative AI. We are looking for a research engineer who is passionate about open-source and excited to create our next-generation post-training software stack. You will work at the intersection of research and engineering, collaborating with the Post-Training and Frameworks teams to invent, implement, and scale the core technologies behind our Nemotron models.

• Work with applied researchers to design, implement and test next generation of RL and pos-training algorithms
• Contribute and advance open source by developing NeMo-RL, Megatron Core, and NeMo Framework and yet to be announced software
• You will be engaged as part of one team during Nemotron models post-training
• 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 computer-architecture, libraries, frameworks, AI applications and the entire software stack.
• Performance tuning and optimizations, model training with mixed precision recipes on next-gen NVIDIA GPU architectures.
• Publish and present your results at academic and industry conferences

• BS, MS or PhD in Computer Science, AI, Applied Math, or related fields or equivalent experience
• 3+ years of proven experience in machine learning, systems, distributed computing, or large-scale model training.
• Experience with AI Frameworks such as Pytorch or JAX
• Experience with at least one inference and deployment environments such as vLLM, SGLang or TRT-LLM
• Proficient in Python programming, software design, debugging, performance analysis, test design and documentation.
• Strong understanding of AI/Deep-Learning fundamentals and their practical applications.
• Ways to stand out from the crowd:
• Contributions to open source deep learning libraries
• 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.