Senior Solutions Architect, Adtech and Media

NVIDIA NVIDIA · Semiconductors · CA +2 · Remote

NVIDIA Solutions Architect focused on AdTech and Media, helping customers adopt NVIDIA's full-stack accelerated computing platform. This role involves technical advisory, proof-of-concept evaluations, deep analysis and optimization of AI/ML models and recommender systems, and translating customer feedback into product insights. The role requires strong Python/C++ coding, understanding of AdTech/MarTech, ML/DL frameworks, and deploying models at scale on cloud or on-premise environments.

What you'd actually do

  1. Perform in-depth analysis and optimization of AI/ML models, recommender systems, and data processing pipelines to ensure peak performance on current- and next-generation GPU architectures.
  2. Interact directly with customer data scientists, engineers, and developers on high-impact projects, using your expertise to help them deploy and scale their solutions.
  3. Act as a trusted technical advisor for customers and partners, conducting proof-of-concept evaluations, and providing deep technical guidance on the best use of NVIDIA hardware and software.
  4. Partner with NVIDIA's engineering, product, and sales teams to secure design wins and drive the adoption of NVIDIA technology within the AdTech and Media distribution ecosystems.
  5. Translate customer feedback into actionable insights for NVIDIA's product and engineering teams to help guide the development of new features and products.

Skills

Required

  • Python
  • C++
  • ML/DL algorithms and frameworks (PyTorch, TensorFlow, Spark, Dask)
  • deploying ML/DL models at scale
  • AdTech, MarTech and Media distribution landscape
  • NVIDIA hardware and software

Nice to have

  • NVIDIA GPU architectures and development tools (CUDA-X, cuBLAS, cuDNN, RAPIDS)
  • MLOps technologies (Docker, Kubernetes)
  • large-scale data processing and distributed systems
  • customer-facing role experience
  • public profile (blogs, GitHub, conference talks)

What the JD emphasized

  • production-level code
  • deploy and scale their solutions
  • deploying ML/DL models at scale

Other signals

  • deploy and scale their solutions
  • deploying ML/DL models at scale
  • performance on current- and next-generation GPU architectures