Solutions Architect, CRISP
2 weeks ago
NVIDIA’s Worldwide Field Operations (WWFO) team is looking for a Generative AI focused Solution Architect with expertise in Machine Learning (ML), Deep Learning (DL) and Data Science platforms for our Consumer Internet Service Provider (CRISP) customers. In our Solutions Architecture team, we work with the most exciting accelerated computing platforms, 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 thinking about artificial intelligence, someone who can maintain synergy in a fast paced, rapidly evolving field, someone able to coordinate efforts between corporate marketing, industry business development and engineering. You will be working with the latest HPC architectures coupled with the most advanced neural network models, changing the way people interact with… technology. As a Solutions Architect, you will be the first line of technical expertise between NVIDIA and our customers. Your duties will vary from working on proof-of-concept demonstrations, to driving relationships with key executives and managers to evangelize accelerated computing and Generative AI. Dynamically engaging with developers, scientific researchers, data scientists, IT managers and senior leaders is a meaningful part of the Solutions Architect role and will give you experience with a range of challenges and solutions. What You’ll Be Doing: Develop and demonstrate solutions based on NVIDIA’s state-of-the-art GenAI software and hardware technologies to customers Work directly with key customers to understand their challenges and provide the best solutions based on NVIDIA products Perform in-depth analysis and optimization to ensure the best performance on GPU-accelerated systems. This includes support in optimization of both training and inference pipelines Partner with Engineering, Product and Sales teams to understand customer’s challenges and plan for best suitable solutions for customers. Enable development and growth of product features through customer feedback and proof-of-concept evaluations Build industry expertise and become a contributor in integrating NVIDIA technology into Enterprise Computing architectures What We Need to See: MS/PhD in Computer Science, Data Science, Electrical/Computer Engineering, Physics, Mathematics, other Engineering fields 3+ years of academic and/or industry experience in fields related to machine learning, deep learning and/or data science Excellent ability to listen, both verbal and written communication skills, and being comfortable with presenting technical solutions in English You are excited to work with multiple levels and teams across organizations (Engineering, Product, Sales and Marketing team) You are a self-starter with aptitude for growth, passion for continuous learning and sharing findings across the team Ways to Stand Out from The Crowd: Experience running and optimizing large scale distributed DL training Expertese in optimization of inference pipeline, using a range of inferencing technics (e.g., understanding of model compression techniques, model compilation or model serving) Background with working with larger transformer-based architectures as well as ability to use DevOps technologies such as Docker, Kubernetes, Singularity, etc Understanding of HPC systems: data center design, high speed interconnect InfiniBand, cluster storage and scheduling related design and/or management experience Experience in deploying large-scale training and inferencing pipeline on Hyperscaler’s infrastructure NVIDIA is a Learning Machine NVIDIA pioneered accelerated computing to tackle challenges no one else can solve. Our work in AI and the metaverse is transforming the world’s largest industries and profoundly impacting society. Learn more about NVIDIA
Company:GB01 NVIDIA United Kingdom
Qualifications: Language requirements: Specific requirements: Educational level: Level of experience (years):Senior (5+ years of experience)
Tagged as: Clustering , Industry , Machine Learning , Neural Networks , NLP , Unspecified
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