Senior AI Solutions Architect - Vision & Diffusion Pipelines

NVIDIA

Lavamünd

Vor Ort

EUR 90.000 - 150.000

Vollzeit

vor 3 Stunden
Sei unter den ersten Bewerbenden
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Zusammenfassung

NVIDIA seeks a Senior Solutions Architect to optimize large-scale training and inference for computer vision models, including diffusion-based approaches, with EMEA AI Native customers. You bridge NVIDIA's generative AI platform and production pipelines, driving performance and scalability across the full content generation lifecycle.

You will guide architectures, optimize multi‑GPU deployments, and provide actionable feedback to research and engineering.

Qualifikationen

  • MS or PhD in Computer Science, Computer Vision, ML or equivalent hands-on experience.
  • 5+ years in AI/ML with deep expertise in vision models.
  • Experience with diffusion model frameworks for image/video generation.
  • Understanding of vision encoder optimization and inference-time tradeoffs.
  • Strong communication with ML researchers and engineers.

Aufgaben

  • Guide AI Native companies in training and deploying pipelines on NVIDIA infrastructure.
  • Provide deep technical guidance on diffusion architectures and multi-GPU deployments.
  • Optimize generation pipelines and ensure efficient inference across platforms.
  • Translate customer insights into actionable feedback for NVIDIA research and engineering.
  • Contribute to the EMEA developer community through demos and workshops.

Kenntnisse

Diffusion models
Computer vision
Large-scale training
Inference optimization
Communication

Ausbildung

MS or PhD in Computer Science

Tools

TensorRT
Triton Inference Server
NIM

Jobbeschreibung

NVIDIA seeks a Senior Solutions Architect to optimize large-scale training and inference for computer vision models, including diffusion-based approaches, with EMEA AI Native customers. You bridge NVIDIA's generative AI platform and production pipelines, driving performance and scalability across the full content generation lifecycle.

You will guide architectures, optimize multi‑GPU deployments, and provide actionable feedback to research and engineering.

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