Overview
Deep Learning Solutions Architect – Large Scale Inference Optimization role at NVIDIA. NVIDIA’s Worldwide Field Operations (WWFO) team is seeking a Solutions Architect with a strong focus on Deep Learning and a deep understanding of neural network inference. With the introduction of NVIDIA Grace CPUs and Grace-Hopper / Grace-Blackwell systems, the CPU has become more tightly integrated into the AI platform than ever before. Innovations such as Chip-to-Chip NVLINK and the significant expansion of the NVLINK domain have enabled a wide range of new neural network architectures and approaches to training and inference.
The ideal candidate will be proficient using tools such as TRT-LLM, vLLM, SGLang or similar, and have strong systems knowledge, enabling customers to fully use the capabilities of the new GB200 NVL72 systems (for example help customers embrace disaggregated inference, work on efficient KV cache offloading or help with inference of new architectures like hybrid or diffusion models). Solutions Architects work with the most exciting computing hardware and software, driving the latest breakthroughs in artificial intelligence! We need individuals who can enable customer productivity and develop lasting relationships with our technology partners, making NVIDIA an integral part of end-user solutions. We are looking for someone always passionate about artificial intelligence, someone who can maintain understanding of a fast paced field, someone able to coordinate efforts between corporate marketing, industry business development and engineering.
What You Will Be Doing
Work directly with key customers to understand their technology and provide the best AI solutions.
Perform in-depth analysis and optimization to ensure the best performance on GPU architecture systems (in particular Grace/ARM based systems). This includes support in optimization of large scale inference pipelines.
Partner with Engineering, Product and Sales teams to develop, plan best suitable solutions for customers. Enable development and growth of product features through customer feedback and proof-of-concept evaluations.
What We Need To See
Excellent verbal, written communication, and technical presentation skills in English.
MS/PhD or equivalent experience in Computer Science, Data Science, Electrical/Computer Engineering, Physics, Mathematics, other Engineering fields.
5+ years work or research experience with Python/ C++ / other software development.
Work experience and knowledge of modern NLP including good understanding of transformer, state space, diffusion, MOE model architectures. This can include either expertise in training or optimization/compression/operation of DNNs.
Understanding of key libraries used for NLP/LLM training (such as Megatron-LN, NeMo, DeepSpeed etc.) and/or deployment (e.g. TensorRT-LLM, vLLM, Triton Inference Server).
Person excited to work with multiple levels and teams across organizations (Engineering, Product, Sales and Marketing team). Capable of working in a constantly evolving environment without losing focus.
Self-starter with demeanor for growth, passion for continuous learning and sharing findings across the team.
Ways To Stand Out From The Crowd
Experience running/debugging large scale distributed DL training or inference.
Proven track record in optimizing neural network training performance and robustness, including implementing asynchronous checkpointing and optimizing CUDA kernels.
Understanding of HPC systems: data center design, high speed interconnect InfiniBand, Cluster Storage and Scheduling related design and/or management experience.
NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
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