Nvidia Senior AI Software Engineer GenAI Framework 

United States, Texas 

692841152

In this critical role, you will expand NeMo Framework's capabilities, enabling users to develop, train, and optimize models by designing and implementing new features and 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, define and implement highly optimized solutions.

What you’ll be doing:

• Design and develop the GenAI open source and .

• Solve large-scale, end-to-end AI training and inference challenges, spanning the full model lifecycle from initial data curation and pre-processing, orchestration and running of model training and tuning, to model deployment.

• Work at the intersection of deep learning applications, libraries, frameworks, and the entire software stack.

• Performance tuning and optimizations of deep learning framework & software components.

• Research, prototype and develop robust and scalable AI tools and pipelines.

What we need to see:

• MS, PhD or equivalent experience in Computer Science, AI, Applied Math, or related field and 5+ years of industry experience.

• Experience with AI Frameworks (e.g. PyTorch, JAX), and/or inference and deployment environments (e.g. TRT, ONNX, Triton).

• 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 deep learning fundamentals and their practical application.

Ways to stand out from the crowd:

• Expertise 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 Generative AI techniques applied to LLM and MM learning (Text, Image, Video, Speech).

• Knowledge of GPU/CPU architecture and related numerical software.

• Experience with cloud computing (e.g. end-to-end pipelines for AI training and inference on CSP (AWS/Azure/GCP/OCI).

• Contributions to open source deep learning frameworks.