Solution Architect – Computational Chemistry and Material Science at NVIDIA

, , United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

17 Jun, 26

Salary

0.0

Posted On

19 Mar, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI Adoption, Proof-of-Concept Development, Benchmarking, Optimization, Technical Advisor, NVIDIA Technology, Use Case Mapping, AI Advancements, Foundation Models, Computational Chemistry, Material Science, Python, AI/ML Frameworks, PyTorch, Langchain, CUDA

Industry

Computer Hardware Manufacturing

Description

Join our team as a Solutions Architect in EMEA, where you will collaborate with Consumer-Packaged Goods (CPG) and Material Science customers focusing on the ground-breaking potential of AI for computational chemistry and material science. At NVIDIA, we have a group of exceptional developers and scientists who thrive on working with the latest GPU hardware and software. Our platform for ML/AI and high-performance computing has already had a significant impact on the industry, serving leading CPG companies, ISVs, and start-ups. We are seeking an enthusiastic individual to join us in exploring new opportunities in the AI-powered revolution in the field of computational chemistry. As a Solution Architect, you will work as a trusted technical advisor to our customers, who envision accelerated computing and artificial intelligence as a transformative force in their field. Join us on this thrilling journey and advance your career while empowering top organizations and institutions worldwide. What you will be doing: Guide customers through the end-to-end process of AI adoption—from requirements gathering and proof-of-concept development to deployment, integration, benchmarking and ongoing optimization Collaborate with our business/account team to identify technical needs, customer goals, and strategies. Your responsibilities will include enabling customer adoption of NVIDIA technology by mapping our solutions to their use cases and driving positive relationships with our technology partners, making NVIDIA an integral part of end-user solutions. Keep up to date on AI advancements in the domain including AI techniques, foundation models for material science and computational chemistry, as well as relevant NVIDIA technology that enable this innovation. Engaging with developers, researchers, data scientists, IT managers, and senior leaders internally and externally is an essential part of the Solutions Architect role to gain experience in various technical areas. Document what you know and teach others. This can vary from building targeted training for partners and other Solutions Architects to writing whitepapers, blogs, and wiki articles to simply working through hard problems with a customer on a whiteboard. We make heavy use of conferencing tools, but some travel is required for this role. You are empowered to find the best way to get your job done and make our customers successful. What we need to see: MS or PhD (or equivalent experience) in Computer Science, Computational Chemistry, Material Science or Computational Physics, or related fields with strong applied experience in these domains. 5+ years of work-related experience with hands-on expertise in AI/ML for chemistry or Material sciences. Proven experience with Python and AI/ML frameworks (PyTorch, Langchain, or building custom framework) and application to scientific questions. Strong time-management and organizational skills for coordinating multiple initiatives, priorities, and implementations of new technology and products into very complex projects. Motivated self-starter with an equal balance of strong problem-solving skills and customer-facing communication skills - especially in effectively presenting complex technical information. Must enjoy engaging with innovative individuals, continuous learning, and staying at the forefront of the field. Ways to stand out from the crowd: Experience building, deploying, and optimizing AI systems for Chemistry and Materials Science end-to-end including data ingestion, preprocessing, model training, pipeline deployment and evaluation, especially for scientific software vendors and data platforms. Hands-on experience with CUDA and PyTorch Experience developing or using Machine Learning Interatomic Potential (MLIP) models for chemical and materials discovery. Working knowledge running MD and DFT simulations. Familiarity with Graph Neural Networks (GNNs) for property prediction of molecules, polymers, and formulations as well as experience in the CSP industry or established thought leadership through publications or presentations on AI/ML applications in Chemistry and Materials Science. 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. NVIDIA is 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. If you are passionate about NVIDIA technology and how it can unlock Digital Biology, we want to hear from you! NVIDIA is the world leader in accelerated computing. NVIDIA pioneered accelerated computing to tackle challenges no one else can solve. Our work in AI and digital twins is transforming the world's largest industries and profoundly impacting society. Learn more about NVIDIA.

Responsibilities

The role involves guiding customers through the end-to-end process of AI adoption, from requirements gathering to deployment and optimization, while collaborating with the business team to map NVIDIA solutions to customer use cases. Responsibilities also include staying current on AI advancements in computational chemistry and material science, engaging with various stakeholders, and documenting knowledge through training, whitepapers, and articles.