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Nvidia Multimodal Deep Learning Solution Architect 
France, Auvergne-Rhône-Alpes 
534647019

15.10.2025
France, Remote
Poland, Remote
Spain, Remote
Switzerland, Remote
Germany, Remote
time type
Full time
posted on
Posted 13 Days Ago
job requisition id

What you will be doing:

  • Serve as the primary technical expert between NVIDIA and our customers, understanding their technology and provide the best AI solutions/ guidance on training process in terms of tools and methodology

  • Build proof-of-concepts and demonstrations that highlight the power of NVIDIA AI platforms for Vision Language Reasoning Models

  • Partner with developers, researchers, technology specialists, IT professionals, and executives to facilitate the integration of NVIDIA technology

  • Partner with Engineering, Product and Sales teams to develop, plan best suitable solutions for customers. Enable development and growth of product features through customer feedback and proof-of-concept evaluations.

What we need to see:

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

  • Deep expertise in AI/Deep Learning, with hands-on experience in training or optimizing VLMs for production

  • Expertise with deep learning frameworks for training VLMs (PyTorch, Nemo), and/or experience with such model's optimization methods and tools (TensorRT and Triton Inference Server).

  • Excellent verbal, written communication, and technical presentation skills in English.

  • 5+ years' work or research experience with Python/ C++ / other software development

  • AI passionate with a growth mindset, ability to collaborate effectively with different teams (Engineering, Product, Sales, Marketing) in a rapid evolving environment while continuously learning and sharing insights.

Ways to Stand Out from The Crowd:

  • Familiarity with Cosmos-Reason and Isaac GR00T

  • Track record in running large scale training and customization of VLM.

  • Track record in Neural Networks inference optimization for Physical AI usecases