Senior Solutions Architect, AdTech and Media

NVIDIA

full-time

Posted on: 9/17/2025

Origin: • 🇺🇸 United States • California, Illinois, New York

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💰 $184,000 - $356,500 per year

Senior

AWSAzureCloudDistributed SystemsDockerGoogle Cloud PlatformKubernetesPythonPyTorchSparkTensorflow

About the role

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
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
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
Interact directly with customer data scientists, engineers, and developers on high-impact projects, using your expertise to help them deploy and scale their solutions
Translate customer feedback into actionable insights for NVIDIA's product and engineering teams to help guide the development of new features and products
Build technical collateral, such as notebooks, blogs, and presentations, that demonstrate the value of NVIDIA's platform for key use cases like real-time bidding, user personalization, and content recommendation

BS/MS/PhD in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, or a related field (or equivalent experience)
10+ years of experience as an ML/Software Engineer or Solutions Architect with a consistent record of writing production-level code in Python and/or C++
Deep understanding of the AdTech, MarTech and Media distribution landscape, including key workflows, common platforms (e.g., DSPs, SSPs, CDPs), and the role of machine learning and data science
Experience with ML/DL algorithms and frameworks such as PyTorch, TensorFlow, Spark, or Dask
Excellent communication and presentation skills
Proficiency in deploying ML/DL models at scale on public cloud platforms (e.g., AWS, GCP, Azure) or on-premise data center environments