At a Glance
- Tasks: Be the go-to tech expert for NVIDIA customers, enhancing their solutions with cutting-edge AI.
- Company: Join NVIDIA, a leader in technology and innovation with a diverse culture.
- Benefits: Enjoy competitive pay, comprehensive benefits, and a supportive work environment.
- Other info: Exciting opportunities for career growth in a dynamic and inclusive workplace.
- Why this job: Make a real impact in AI while collaborating with top professionals in the field.
- Qualifications: Deep expertise in AI, especially diffusion models, and strong communication skills.
The predicted salary is between 80000 - 100000 £ per year.
NVIDIA’s Worldwide Field Operations (WWFO) team is seeking a Solutions Architect with deep expertise in Diffusion Models. You will work as the primary technical specialist for selected NVIDIA customers. Your task is to help our customers and make their solutions more efficient, usable or economic by driving adoption of state of the art compute and software platforms. A key part of this role involves close collaboration with a wide range of team members including developers, data scientists, IT managers, and senior executives. The ideal candidate is an experienced AI specialist with a deep understanding of generative AI diffusion models, data selection and their training.
What you will be doing:
- Serve as the primary technical expert between NVIDIA and our ecosystem.
- Partner externally with developers, researchers, technology specialists, IT professionals and executives to facilitate the integration of NVIDIA technology.
- Partner internally with engineering, product and research.
What we need to see:
- Deep expertise and hands-on in AI/Deep Learning specifically with diffusion networks and the intersection of image/video/text processing.
- MS/PhD or equivalent experience in Computer Science, Data Science, Electrical/Computer Engineering, Physics, Mathematics, other Engineering fields.
- Excellent verbal, written communication, and technical presentation skills in English.
- 6+ years' work or research experience with Python/ C++ / other software development.
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.
Solutions Architect, GenAI employer: Nvidia
NVIDIA is an exceptional employer, offering a vibrant work culture that fosters innovation and collaboration among talented professionals. With a focus on cutting-edge technology in AI and cloud systems, employees benefit from competitive salary packages and ample opportunities for personal and professional growth in a dynamic environment. Join us to be part of a team that is not only solving significant challenges but also shaping the future of technology.
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We think this is how you could land Solutions Architect, GenAI
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We think you need these skills to ace Solutions Architect, GenAI
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Nvidia. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
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Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
