Senior AI Solutions Architect
vor 1 Monat
NVIDIA’s Worldwide Field Operations (WWFO) team is looking for a Data Science focused Solution Architect with expertise in Machine Learning (ML), Deep Learning (DL) and Data Science platforms. Exposure to inferencing technology (e.g., understanding of model compression techniques, model compilation or model serving) would be an added value. In our Solutions Architecture team, we 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 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. 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 partners and concerns.
What You’ll Be Doing:
Develop and demonstrate solutions based on NVIDIA’s state-of-the-art AI and ML software and hardware technologies to customers.
Work directly with key customers to understand their technology and provide the best solutions.
Perform in-depth analysis and optimization to ensure the best performance on GPU architecture systems. This includes support in optimization of both training and 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.
Build industry expertise and become a contributor in integrating NVIDIA technology into Enterprise Computing architectures.
What We Need to See:
Excellent verbal, written communication, and technical presentation skills in English.
MS/PhD in Computer Science, Data Science, Electrical/Computer, or equivalent experience Engineering, Physics, Mathematics, other Engineering fields
8+ years of academic and/or industry experience in fields related to machine learning, deep learning and/or data science.
You are excited to work with multiple levels and teams across organizations (Engineering, Product, Sales and Marketing team)
You are a self-starter with attitude for growth, passion for continuous learning and sharing findings across the team.
Ways to Stand Out from The Crowd:
Experience running large scale distributed DL training.
Background with working with larger transformer-based architectures.
Experience using DevOps technologies such as Docker, Kubernetes, Singularity, etc.
Understanding of HPC systems: data centre design, high speed interconnect InfiniBand, cluster storage and scheduling related design and/or management experience.
With competitive salaries and a generous benefits package, we are widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us and, due to unprecedented growth, our exclusive engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, we want to hear from you 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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