Solution Architect, Earth-2 NVIS- New College Graduate
Switzerland, Zurich
NVIDIA
NVIDIA erfindet den Grafikprozessor und fördert Fortschritte in den Bereichen KI, HPC, Gaming, kreatives Design, autonome Fahrzeuge und Robotik.NVIDIA is building Earth-2, a platform to accelerate production of digitals twin of the Earth combining GPU-accelerated computing; deep learning and breakthroughs in physics-informed neural networks; machine learning emulation of physics predictions, along with vast quantities of observed and model data to learn from.
NVIDIA aims at harnessing the power of artificial intelligence to substantially mitigate climate change. The Earth-2 Solution Architect team will support efforts in the fields of weather and climate risk modeling, mitigation, monitoring, adaptation, disaster response, and weather-extremes. Solution architects work closely with customers and help them deploy our solution stack on their systems. Academic and commercial groups around the world are using NVIDIA products to revolutionize deep learning and data analytics, and to power data centers. We are looking for a solution architect who can work jointly with the NVIDIA Infrastructure Specialists Team to deploy Earth-2 on the largest and fastest AI/HPC systems.
What you'll be doing:
- Support the deployment of Earth-2 on large clusters and work closely with the NVIDIA Infrastructure Specialists Team
- Support customers in using our products. Help developing solutions aligned with customer’s requirements
- Work with multiple teams: Engineering, Product, Sales and Marketing in a constantly evolving environment with ability to multitask in a fast-paced environment
- Work on building digital twins based on NVIDIA technology
- Work with large datasets, including real-time geospatial data and historical records, to identify vulnerabilities and quantify potential impacts
- Stay up-to-date with the latest AI/ML/Digital Twin innovations
- Deliver trainings, hackathons and demos on using our solutions and platforms
What we need to see:
- AI/ML expertise, MS/PhD degree in Environmental risk, Climate Science, Computational Physics, Computer Science, or related technical field
- 3+ years of industrial or academic experience with good programming skills (Python and/or C/C++)
- Experience in weather and climate risk modeling with focus on AI models, and data pipelines
- Knowledge of cloud platforms and high-performance computing systems
- Experience in digital twin technologies
- Experience in developing or using major deep learning frameworks (e.g., PyTorch)
- Familiarity with containers, numerical libraries, modular software design, version control
- Willingness to learn NVIDIA platforms, in particular NVIDIA Modulus and NVIDIA Omniverse
- Solid written and oral communications skills and familiarity with collaborative environments
- Team players, creative abilities, can learn and adapt quickly
Ways to stand out from the crowd:
- Understanding of HPC systems: data center design, high speed interconnect InfiniBand, Cluster Storage and Scheduling related design and/or management experience
- Experience in industrial applications of the climate science to climate-tech, risk modeling, agri-tech, financial sectors, weather forecasting, renewables
- Running, analyzing or visualizing output from comprehensive Earth System Models
NVIDIA is one of the technology industry's most desirable employers. If you're a creative person with a genuine passion for AI and willing to contribute to Society’s Climate Change challenge, we want to hear from you. NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. We encourage diversity in candidates’ profiles. If you think you have the relevant skills and experience for this job, we encourage you to apply even if you lack some of the requirements listed above.
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Computer Science Data Analytics Data pipelines Deep Learning Engineering GPU HPC Industrial InfiniBand Machine Learning PhD Physics Pipelines Python PyTorch
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