Latest UK roles, updated daily.
As a Solutions Architect, you will lead technical discovery and build reference architectures for enterprise-ready agentic AI products using NVIDIA's stack. You will collaborate with partners to optimize AI workflows, create proofs of concept, and develop reusable assets like benchmark reports and playbooks.
As a Solutions Architect, you will lead technical discovery with AI application wrapper partners, build reference architectures, and create proofs of concept using NVIDIA's stack. You will optimize agentic workloads, support multi-stage deployments, and develop reusable assets for agentic applications.
As a Solutions Architect, you will lead technical discovery and build reference architectures for AI application wrappers, orchestrating agents, and specialized workflows using NVIDIA's stack. You will work closely with partners to develop enterprise-ready agentic AI products, optimize workloads, and create reusable enablement assets.
NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people.Today, we’re tapping into the unlimited potential of AI to define the...
As a Solutions Architect, you will lead technical discovery and build reference architectures for enterprise-ready agentic AI products on the NVIDIA stack. You will collaborate with partners to develop proofs of concept, optimize workloads, and create reusable assets like benchmark reports and playbooks.
A Data Scientist in the Delivery team will work closely with Simulation and Machine Learning Engineers to transform AI models into practical engineering tools. You'll preprocess and analyze complex data, build deep learning models combined with optimization methods, and develop scalable data pipelines for production. The role involves direct collaboration with customers across industries like Aerospace, Energy, and Semiconductors, including on-site work globally, ensuring seamless integration of data science solutions with physics-based simulations.
This role involves owning and evolving a high-performance computing (HPC) infrastructure to support CPU/GPU-intensive physics simulations, with a focus on performance tuning, job scheduling, and capacity planning. The IT Lead Engineer will also mentor junior staff, automate IT processes, and act as the escalation point for complex technical issues across HPC, developer tooling, and general IT. The position operates in a high-ownership environment within a deep-tech scale-up, requiring strong Linux, networking, and zero-trust security expertise alongside an automation-first mindset.
Build and improve the AI engine powering interactive roleplay training with AI avatars, focusing on real-time performance scoring, feedback generation, and dynamic response logic. Work on applied machine learning models integrated into a product-driven environment, collaborating closely with engineering and product teams to enhance learning-by-doing experiences. The role emphasizes fast iteration and delivering tangible impact through scalable AI systems.
Lead the technical strategy for large-scale AI inference deployments with EMEA-based AI-native customers, from proof of concept through to production. Architect and optimize high-performance inference pipelines using NVIDIA's stack, including TensorRT-LLM and Dynamo, while translating real-world deployment challenges into product feedback. Collaborate across NVIDIA teams and customer organizations to drive scalable, efficient AI inference solutions addressing latency, cost, and GPU utilization.
Lead technical strategy for AI inference deployments across EMEA, working closely with AI-native enterprises and frontier labs to optimize large-scale neural network inference on NVIDIA's platform. Architect high-performance inference pipelines using frameworks like TensorRT-LLM, vLLM, and SGLang, and translate real-world deployment challenges into product improvements. Focus on solving critical issues around latency, efficiency, memory utilization, and GPU cluster performance.
This role involves leading AI inference strategy for major EMEA clients, focusing on optimizing large-scale neural network deployments. You'll architect high-performance inference pipelines using NVIDIA's latest technologies, solve critical challenges in latency, efficiency, and scalability, and translate real-world deployment insights into product improvements. The position bridges technical leadership, customer engagement, and product development within NVIDIA's AI ecosystem.
This role involves leading the technical strategy for large-scale AI inference deployments across EMEA, working closely with AI-native customers and internal teams at NVIDIA. You will architect high-performance inference pipelines using NVIDIA's stack, optimize for latency and efficiency, and translate real-world deployment challenges into product improvements. The position requires deep expertise in LLM/VLM inference, transformer optimization, and scalable AI infrastructure.
This role involves leading AI inference strategy across EMEA by collaborating with frontier AI labs and enterprises to solve scalability challenges in production deployments. The Senior Solutions Architect will optimize high-performance inference pipelines using NVIDIA's stack, including TensorRT-LLM and Dynamo, while translating customer insights into product improvements. Focus areas include latency, efficiency, GPU utilization, and deployment at scale.
Design and implement GPU-accelerated solutions for hyperscaler environments, with a primary focus on Google Cloud Platform. Develop proof-of-concepts and optimize GenAI, machine learning, and data science workloads across training and inference pipelines. Collaborate with engineering, product, and sales teams to integrate NVIDIA technology into enterprise computing architectures and support developer adoption through technical engagement and performance optimization.
Design and deploy production-grade NLP systems and large language models tailored for financial services, with a focus on scalability, data governance, and regulated environments. Develop fine-tuned, low-latency AI workflows integrated into cloud infrastructure, while mentoring engineers and advancing evaluation methodologies for LLM outputs. Work across the full ML lifecycle from research to deployment in a remote-first, innovation-driven team.
