What You’ll Be Doing:

A considerable part of the day-to-day job is staying up to date on pioneering Deep Learning and Machine Learning ecosystems. You'll be called on to help architect and scale high-performance, distributed AI deployments on-prem or in the cloud built with the latest NVIDIA GPU supercomputers.

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 working through challenging problems with a partner on a whiteboard.

Answer questions and provide mentorship. Work with Partner Business Managers to assist partners and customers on their critical projects. You will help them build their GPU and DPU-enabled Accelerated Compute datacenters or cloud services to get the most out of their investment.

Lead and develop proofs-of-concept (PoCs) for solutions applied to enterprise and industrial applications such as LLM, NLP/NLU, recommender systems, image recognition, video analytics, and DPU applications.

Support the business development team through the sales process for GPU/DPU/Network hardware/software products. Responsible for technical relationships and enabling customers to build innovative NVIDIA technology solutions.

Partner with NVIDIA Engineering, Product, and Sales teams to secure design wins for customers. Enable development and growth of NVIDIA product features through customer feedback and PoC evaluations.

What We Need To See:

BS, MS, or PhD in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, or other Engineering fields or equivalent experience.

5+ years of work-related experience in Deep Learning and Machine Learning, including deep learning frameworks TensorFlow or PyTorch, GPU, and CUDA experience extremely helpful.

Experience working with DevOps on-prem or in cloud environments, including but not limited to Docker/Containers, Kubernetes, cloud APIs, IaaS and Data Center deployments; additional experience with DPUs applications development will be beneficial.

Deep understanding of dense data center design, including computing, storage, networking, cloud APIs, and IaaS.

Ability to multitask efficiently in a dynamic environment.

Strong analytical and problem-solving skills.

Clear written and oral communication skills with the ability to effectively collaborate and coordinate across cross-functional teams in engineering, sales, marketing, product, and program management.

Comfortable working in a customer-facing environment.

C/C++ and Python programming skills.

Ways To Stand Out From The Crowd:

Excellent customer-facing skills and background.

Skilled in deploying ML/DL models at scale on cloud computing clusters in production.

Development experience with NVIDIA software libraries and GPUs or DPUs.

Knowledge of LLM, MLOps, DevOps, and Cloud-oriented workflows using Docker/containers, Kubernetes, cloud APIs, data center deployments, etc.

Able to think creatively to debug and solve complex problems.