Senior Deep Learning Solution Architect
Nvidia
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Interviews for architect roles keep coming back to AWS, Python, Azure, GCP. Practise those questions before you sit with Nvidia.
Questions you are likely to be asked
- Why do you want to join Nvidia?
- What is your experience with LLMs? Tell me one thing you learned the hard way.
- How would you explain your model's result to someone who is not technical?
- What would you check first if a model's accuracy dropped after going live?
- When would you not use machine learning for a problem?
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Practise the Senior Deep Learning Solution Architect at Nvidia interview free →NVIDIA is leading company of AI computing. At NVIDIA, our employees are passionate about AI, HPC , VISUAL, GAMING. Our SA team is more focusing to bring NVIDIA new technology into difference industries. We help to design the architecture of AI computing platform, analysis the AI and HPC applications to deliver our value to customers, focusing on defining and solving computational challenges in LLM inference and training acceleration, as well as network communication and data transfer optimization. What You'll Be Doing: Contribute to the development of open-source inference frameworks such as SGLang and vLLM, including feature and operator development, performance optimization, and model support, in collaboration with the community. Develop and optimize KV cache offloading frameworks for LLM workloads, supporting multi-level cache offloading and reuse across CPU, SSD, and remote storage to improve inference efficiency. (Team project: FlexKV) Drive R&D on compute performance in distributed training, and explore methods and technologies for performance optimization. Study computational challenges in machine learning systems, identify common needs and bottlenecks, and build example code, acceleration libraries, or frameworks accordingly. What We Need to See: Over 5 years working experience in the technology industry, with master’s degree or above in computer science, mathematics, electrical engineering, automation, or related fields. Strong interest in accelerated computing, parallel computing, and heterogeneous computing, with the motivation to explore these areas in depth. Solid programming skills, with a good understanding of data structures and computer systems fundamentals. Strong learning agility, adaptability, and the ability to analyze, define, and independently explore technical problems. Ways to Stand Out from the Crowd: Familiarity with heterogeneous computing, distributed training, parallel computing, or other areas related to high-performance computing. Experience in performance analysis, performance modeling, or performance optimization; contributions to open-source frameworks are a plus. Strong ability to define new problems and explore solutions; candidates with independent PhD-level research experience are preferred. Proficiency with AI coding tools. With competitive salaries and a generous benefits package, we are widely considered to be one of the 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 outstanding growth, our best-in-class engineering teams are rapidly growing. If you're a creative and autonomous person with a real passion for technology, we want to hear from you.
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Listed on workday · posted 2026-09-30. ApplySarthi collects openings and links to application pages; the role is advertised by Nvidia, not by us.