Solutions Architect, Manufacturing
NVIDIA is hiring a Solutions Architect, Manufacturing in Tokyo, Japan. Level rates it ; you can apply on NVIDIA.
Level's read on this job
- Works on AI ●●●○ is how Level rates this solutions architect role: the daily work is on AI products, without building the model.
- The posting mentions PyTorch, CUDA and Alchemi among its tools and skills.
- Level classes it as a senior role.
- NVIDIA has 2,700+ other open roles on Level.
AI in this role
Provide technical solutions and guidance to manufacturing companies adopting GPU acceleration and AI workflows.
NVIDIA is a world leader in computer graphics, artificial intelligence, and accelerated computing. For over 25 years, NVIDIA has been at the forefront of research and engineering around the greatest advances in technology. Our history of innovation drives us to solve the world's hardest problems.
We are now seeking a Solution Architect to support Japanese manufacturing companies adopt GPU-acceleration and AI for Chemical and Materials Science. The use of AI for discovering novel chemicals, materials, and formulations is a growing area in Industry and Academia. We need someone with domain knowledge and great communication skills to be a trusted technical advisor to our customers and work on exciting projects focused on Industrial High-Performance Computing (HPC) and AI for Chemistry and Materials Science.
What you’ll be doing:
Be a technical expert with vision on integrating NVIDIA GPU-accelerated tools and microservices such as ALCHEMI, PhysicsNemo, and our CUDA-X libraries into Industrial HPC, Chemistry, Material Science, and Semiconductor Manufacturing workflows.
Support the business development team in guiding the customer through the sales process for NVIDIA’s platform of GPU compute systems and acceleration software. Be responsible for the technical relationship and assist our customers and partners in developing innovative products using NVIDIA technologies.
Dynamically engage with developers, scientific researchers, and data scientists to understand their goals, strategies, and technical needs, and help to define and deliver high-value solutions that meet these needs. Answer questions and provide mentorship – you will help them build their GPU compute infrastructure and get the most out of their investment.
Document what you know, and guide others. This can vary from building targeted training for partners and other Solution Architects, to writing whitepapers, blogs, and wiki articles, to working through challenging problems with a partner on a whiteboard.
Keep up-to-date on the latest techniques in AI for Chemistry and Materials Science, including atomistic simulation, machine learning interatomic potentials (MLIPs), graph neural networks (GNNs) for property prediction, and structural generative AI models.
What we need to see:
Excellent verbal, written communication, and technical presentation skills in Japanese. Business level English communication is also a requirement.
BS or MS in Computational Chemistry, Physics, Computer Science, or related field (or equivalent experience)
5+ years of industry or academic experience in the manufacturing ecosystem, applying the latest advances in AI for Chemical and Material Science
Strong fundamentals in programming, optimizations and software design, especially in Python and C++
Ability to multitask effectively in a dynamic environment.
Strong analytical and problem-solving skills.
Proactive and have a strong desire to share knowledge with clients, partners and co-workers.
Ways to stand out from the crowd:
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.
Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family at www.nvidiabenefits.com/.
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.
How we rate this
Solutions Architect, Manufacturing at NVIDIA rates 65 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Pay
- Typical solutions architect job
- $169k
This posting doesn't list pay. Use the typical solutions architect figure when pay comes up. See solutions architect jobs
Questions you could be asked
- Tell me about a project where hpc was part of your work. What did you do?
- Tell me about a project where gpu acceleration was part of your work. What did you do?
- Tell me about a project where solutions architecture was part of your work. What did you do?
- Tell me about a project where chemistry was part of your work. What did you do?
- Tell me about a project where materials science was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: Hpc, GPU Acceleration, Solutions Architecture, Chemistry, and Materials Science. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.
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