Nvidia Senior Solutions Architect AI ML 

United States, California 

292947569

What you’ll be doing:

• As a key technical member of a focused account team, you will serve as the main point of contact for NVIDIA products, enabling internet giants and cloud providers to have an innovative AI/ML software infrastructure.

• Work directly with outstanding engineering teams to secure design wins, address challenges, bring solutions to production, and support them throughout their lifecycle.

• Become a trusted advisor to your customer by understanding their environment, constraints, and long-term strategy. Translate these insights into product requirements and innovative solutions.

• Help your customer enhance the value of NVIDIA technology, and provide feedback to NVIDIA for future product improvements.

• Facilitate the resolution of customer issues, offering timely and proactive communications to mitigate risks.

• Lead workshops, demos, and proof-of-concepts to showcase NVIDIA’s AI/ML capabilities.

• Guide customers on standard processes for scalable AI model deployment and inference optimization.

What we need to see:

• Minimum of a BS/MS in Computer Science, Electrical Engineering, or equivalent experience.

• 8+ years of engineering experience with a proven record in AI/ML-focused projects or enterprise-grade solutions.

• Solid understanding of Linux, including fixing, optimization, and customization for AI/ML workloads.

• Strong understanding of data science and machine learninginfrastructure—softwareand hardware.

• Excellent follow-up and interpersonal skills, with a true passion for problem-solving.

• Proficient in Python, with the ability to develop scripts and build custom tools. Experience with parallel programming or GPU acceleration (e.g., CUDA) is helpful.

Ways to stand out from the crowd:

• Background with Chatbots, RAG pipelines, vector databases, and distributed training or inference workloads.

• Experience or background in HPC (High Performance Computing) environments for AI or ML applications.

• Familiarity with multi-node GPU clusters and performance tuning for large-scale AI workloads.

• Experience developing in cloud and/or virtualized environments, containerized solutions, with knowledge of Docker, Kubernetes

• Deep learning and AI experience with common deep learning frameworks such as PyTorch or TensorFlow.

We make extensive use of conferencing tools, but occasional travel is required for local on-site visit to customers and industry events.