This role involves owning full-solution validation, minimizing time to production, and ensuring Day 2 stability for large-scale GPU clusters. You will work closely with customers, partners, and cross-functional teams to architect, implement, and optimize Kubernetes-based platforms and HPC systems.
This role involves owning full-solution validation, minimizing time to production, and ensuring Day 2 stability for large-scale GPU clusters. You will work closely with customers, partners, and cross-functional teams to architect and implement Kubernetes-based platforms, automate processes, and provide consultative guidance across the entire stack.
This role involves developing and advancing AI systems for autonomous driving, focusing on areas like world modeling, reinforcement learning, and spatial intelligence. You'll work on cutting-edge research and real-world deployment, contributing to the future of embodied AI.
This role involves advancing embodied AI for autonomous driving through research in world modeling, reinforcement learning, and 3D spatial understanding. The scientist will develop foundation models, scalable decision-making systems, and sim-to-real transfer techniques, working at the intersection of machine learning, robotics, and simulation. The position emphasizes long-horizon prediction, cross-embodiment learning, and evaluation framework development in a research-driven environment.
This role involves guiding AI-native customers and enterprises in deploying and optimizing large-scale AI inference workloads on multi-node GPU clusters. The focus is on architecting efficient inference pipelines for dense and sparse Mixture-of-Experts (MoE) models, optimizing performance through techniques like quantization, speculative decoding, and KV cache management. The position requires deep technical collaboration with both customers and NVIDIA's product teams to advance high-performance inference solutions across EMEA.
Design and optimize large-scale AI inference systems on multi-node GPU clusters, focusing on MoE models, memory efficiency, and interconnect-aware scheduling. Collaborate with AI teams and NVIDIA product groups to solve high-performance computing challenges in production environments. Lead technical engagement through workshops and reference architectures for EMEA customers.
This role involves guiding AI-native customers in deploying and optimizing large-scale inference workloads on multi-node GPU clusters, with a focus on architecting efficient pipelines for dense and sparse Mixture-of-Experts models. The Senior Solutions Architect will tackle challenges in inference efficiency, including quantization, speculative decoding, and KV cache management, while collaborating with NVIDIA product teams and engaging the EMEA developer community through technical leadership and knowledge sharing.
Design and optimize large-scale AI inference pipelines for multi-node GPU clusters, focusing on MoE models, memory efficiency, and interconnect-aware scheduling. Collaborate with product teams and lead technical engagements with AI-native enterprises. Drive innovation in inference performance through workshops and reference architectures.
This role involves guiding AI customers in deploying and optimizing large-scale inference workloads on multi-node GPU clusters, with a focus on advanced techniques like MoE serving, speculative decoding, and memory optimization. The architect will design efficient inference pipelines for dense and sparse models across thousands of GPUs and collaborate with NVIDIA’s product teams to enhance customer success. The position also includes leading technical engagement through workshops and reference architectures across EMEA.
This role involves designing and optimizing large-scale AI inference solutions on multi-node GPU clusters, focusing on advanced techniques like Mixture-of-Experts (MoE) serving, speculative decoding, and memory-bound workload optimization. The Senior Solutions Architect will work closely with leading AI organizations and NVIDIA’s product teams to drive performance, efficiency, and adoption of NVIDIA's inference stack. Responsibilities include technical leadership, customer engagement, and community building through workshops and reference architectures.
This role involves leading technical partnerships with frontier AI labs across EMEA to integrate NVIDIA's full stack—from GPUs to software libraries—into advanced AI workloads including training, inference, and emerging domains like agents and robotics. The individual will diagnose performance bottlenecks, drive platform optimisations, and translate research trends into product roadmap influence. It’s a strategic position bridging deep technical engagement with product strategy in the fast-evolving generative AI landscape.
This role involves building deep technical partnerships with leading AI research labs and model builders to integrate NVIDIA's GPU hardware, systems, and software stack into their AI development workflows. The candidate will identify performance bottlenecks, drive platform optimisations across training, post-training, and inference, and translate technical insights into product roadmap influence. They will also help shape collaboration strategies, support technical assessments of emerging AI labs, and enable public showcases of joint innovation at events like GTC.
Design and demonstrate GPU-accelerated solutions leveraging NVIDIA and GCP technologies for generative AI and machine learning workloads. Collaborate with developers, data scientists, and technical leaders to optimize training and inference pipelines on hyperscaler infrastructure. Act as a technical bridge between NVIDIA, Google Cloud, and enterprise customers to drive adoption of accelerated computing in AI.
This role involves designing and demonstrating GPU-accelerated solutions for Generative AI and machine learning workloads on hyperscaler platforms, with a primary focus on Google Cloud (GCP). The Solutions Architect will collaborate with NVIDIA’s partners and customers to optimize training and inference pipelines, provide technical leadership, and translate developer needs into product improvements. It emphasizes deep technical engagement across engineering, product, and sales teams to drive adoption of NVIDIA’s AI platforms.
