Solution Architect, Computer Vision - Media and Entertainment

Nvidia
München

This position is at Nvidia

The selection process will be fully managed by Nvidia.

This opportunity is available in Courbevoie - France, Zurich - Switzerland, Munich - Germany.

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Are you passionate about redefining how the world creates, consumes, and analyzes video content? We are looking for a Solutions Architect to join NVIDIA’s EMEA Media & Entertainment (M&E) Team, focusing on the intersection of traditional Computer Vision and the frontier of Generative AI. In this role, you will help leading ISVs and M&E organizations build AI platforms that bring intelligence to every frame—from real-time video analytics and automated summarization to AI-driven marketing content generation that blends 2D and 3D assets. You will combine your deep expertise in detection, tracking, and content generation powered by AI with NVIDIA’s top GPU and AI platforms. This role aims to drive measurable impact across global media ecosystems. We need a creative, hands-on individual to help our partners navigate the challenges of deploying state-of-the-art models at scale, whether on Edge devices for real-time analysis or across Large Cloud deployments for massive content management. **What you will be doing:** * You will work on our EMEA M&E Solution Architects Team to drive NVIDIA technology adoption at key M&E customers. You will secure builds in Data Center, Edge, and Cloud Deployments. * Become a trusted technical advisor for our EMEA-based customers, helping them architect end-to-end agentic pipelines like video analytics, archive analysis, and automated metadata extraction, etc… * Lead customer proof-of-concepts (PoCs) using next-generation AI platforms to solve complex M&E use cases, including automated video summarization, agentic and Generative AI workflows for image and video synthesis. * Design and implement scalable inference solutions that manage the transition from Edge-based processing to massive Cloud clusters, addressing the unique throughput and latency challenges of VLMs and Diffusion models. * Partner with NVIDIA Engineering, Product, and Sales teams to optimize AI techniques bridging algorithms and systems, providing critical field feedback to influence NVIDIA’s future M&E software and hardware roadmaps. * Drive adoption of NVIDIA’s Accelerated Compute Platforms, focusing on growing our customers’ capabilities in deploying modern AI architectures, from traditional CNNs to transformer-based generative models. **What We Need to See:** * MS or PhD in Computer Science, Computer Vision, Engineering, or related technical field (or equivalent experience). * 5+ years of experience in Computer Vision, with a strong foundation in traditional Detection, Segmentation, and Tracking algorithms. * Hands-on experience developing or implementing Vision Language Models (VLMs) and/or Generative AI models applied to visual and video content creation (e.g., Stable Diffusion, GANs, or Transformer-based synthesis). * Proficiency with ML frameworks like PyTorch and a deep understanding of the challenges involved in model deployment, customization, quantization and inference at scale. * Experience managing AI workloads across diverse environments, from resource-constrained Edge devices to high-performance Cloud infrastructure. * Excellent presentation skills with the ability to bridge the gap between deep technical discussions with engineers and value-based conversations with executives. **Ways to Stand Out from the Crowd:** * Expertise with NVIDIA-specific SDKs such as DeepStream, TensorRT, Triton Inference Server, TAO, and the Holoscan or NeMo frameworks. * Experience with 3D rendering, lighting or familiarity with scene creation tools like NVIDIA Omniverse. * Strong background in containerization (Docker/K8s) and optimizing distributed inference for Large Vision Models. We have some of the most forward-thinking and dedicated people in the world working for us. If you're creative and autonomous, we want to hear from you.

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[#LI-REMOTE] [#J-MCITY]

Veröffentlicht am 2026-07-03

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