1 to 25 of 148 NVIDIA Jobs in England

MLOPs pipeline Good to Have Skills GenAI Exposure:Experience with LLMs, LangChain, HuggingFace, synthetic data generation, and prompt engineering. Digital Twin Integration:Familiarity with nVidia Omniverse, AWS TwinMaker, Azure Digital Twin or similar platforms and concepts Visualization Tools:Power BI, Grafana, or custom dashboards for operational insights. DevOps & Automation

Linux-based systems. Desirable Skills Familiarity with UML and tools such as IBM Rhapsody. Experience using MATLAB and Python for data analysis. Knowledge of NVIDIA CUDA programming. Exposure to OpenDDS or other middleware communication frameworks.

Linux-based systems. Desirable Skills Familiarity with UML and tools such as IBM Rhapsody. Experience using MATLAB and Python for data analysis. Knowledge of NVIDIA CUDA programming. Exposure to OpenDDS or other middleware communication frameworks.

following tools and platforms: Matlab and Simulink Python Deep Learning libraries (e.g. Pytorch, scikit-learn, ONNX) C/C++ Hardware development boards (e.g. NVIDIA Jetson products) Alongside involvement with departmental activities, you will be part of a wider cohort of summer placements and graduates on the Early Careers Programme; benefits

Specification: Solution Architect - NVIDIA Cluster (End-to-End Design & Validation) Location: London (1 day per week onsite) Travel: Occasional travel to datacenter sites outside the UK Engagement: Contract Inside IR35 Department: Engineering/Advanced Compute Role Overview We are seeking a highly skilled Solution Architect with deep experience in designing … validating, and delivering end-to-end NVIDIA GPU clusters in enterprise and hyperscale environments. This individual will own the full life cycle of architectural design-from requirements gathering through implementation oversight and performance validation. They will work closely with engineering, networking, DevOps, security, and datacenter operations teams to ensure high

robotic middleware and interfaces (e.g., ROS 2 or similar) Contribute to and leverage simulation, modelling, and validation of robotic systems in virtual environments (e.g., Nvidia Isaac, Gazebo) and physical labs. Collaborate with cross-disciplinary teams to combine software, hardware, and intelligence. Contribute to the full engineering lifecycle, including architecture

C++ (C++14 or later) Hands-on expertise with ROS2 (Robot Operating System 2). Experience integrating software with embedded or edge computing hardware (e.G. NVIDIA Jetson/Xavier). Exposure to ROS1 migration, simulation tools (e.G. Gazebo, RViz), and project tracking tools such as Jira. This role offers a competitive

C++ (C++14 or later) Hands-on expertise with ROS2 (Robot Operating System 2). Experience integrating software with embedded or edge computing hardware (e.G. NVIDIA Jetson/Xavier). Exposure to ROS1 migration, simulation tools (e.G. Gazebo, RViz), and project tracking tools such as Jira. This role offers a competitive

C++ (C+ or later) Hands-on expertise with ROS2 (Robot Operating System 2). Experience integrating software with embedded or edge computing hardware (e.G. NVIDIA Jetson/Xavier). Exposure to ROS1 migration, simulation tools (e.G. Gazebo, RViz), and project tracking tools such as Jira. This role offers a competitive

Solutions : Design and build complex, full-stack solutions from inception through to production. This includes engineering agentic workflows in Python and integrating LLMs from Nvidia, Writer, OpenAI/ChatGPT, and Google Gemini. Integrate with the Commerce Ecosystem : Architect data flows and integrations between AI/ML models and enterprise experience

data centre capacity by 2030, the company is building the physical backbone for a new generation of compute-intensive workloads. The business is an NVIDIA Cloud Partner and is a key organisation in the global on-demand GPU market. Your new role As Data Centre Operations Manager

data centre capacity by 2030, the company is building the physical backbone for a new generation of compute-intensive workloads. The business is an NVIDIA Cloud Partner and is a key organisation in the global on-demand GPU market. Your new role As Data Centre Operations Manager

Person: Buyer, Purchaser, Procurement Specialist or similar PC Parts, Computer Components or IT Hardware background Job Reference: BBBH 22167d Buyer, Procurement, PC, Components, Hardware, Nvidia, AMD, Gaming, Workstation, Build, Microsoft, ASUS, Dell, HP, ASRock, Import, Export, Wirral, Ellesmere Port, Liverpool If you're interested in this role, click 'apply

delivering high-quality products and services to businesses, education, and healthcare sectors. With trusted partnerships with major vendors like HP, Lenovo, Dell, Adobe, and NVIDIA, they are expanding their reach and looking to grow their team. Due to their continued success, they are now seeking a dynamic Sales Executive

network layers in production. Kubernetes. Operate and troubleshoot K8s clusters, and understand how physical resources are abstracted up the stack to K8s. GPU platforms (NVIDIA and AMD). Practical experience with GPU drivers and GPU logs investigation tools, e.g. nvidia-smi. Performance diagnostics using NCCL on large scale clusters. Observability

network layers in production. Kubernetes. Operate and troubleshoot K8s clusters, and understand how physical resources are abstracted up the stack to K8s. GPU platforms (NVIDIA and AMD). Practical experience with GPU drivers and GPU logs investigation tools, e.g. nvidia-smi. Performance diagnostics using NCCL on large scale clusters. Observability

network layers in production. Kubernetes. Operate and troubleshoot K8s clusters, and understand how physical resources are abstracted up the stack to K8s. GPU platforms (NVIDIA and AMD). Practical experience with GPU drivers and GPU logs investigation tools, e.g. nvidia-smi. Performance diagnostics using NCCL on large scale clusters. Observability

network layers in production. Kubernetes. Operate and troubleshoot K8s clusters, and understand how physical resources are abstracted up the stack to K8s. GPU platforms (NVIDIA and AMD). Practical experience with GPU drivers and GPU logs investigation tools, e.g. nvidia-smi. Performance diagnostics using NCCL on large scale clusters. Observability

network layers in production. Kubernetes. Operate and troubleshoot K8s clusters, and understand how physical resources are abstracted up the stack to K8s. GPU platforms (NVIDIA and AMD). Practical experience with GPU drivers and GPU logs investigation tools, e.g. nvidia-smi. Performance diagnostics using NCCL on large scale clusters. Observability

network layers in production. Kubernetes. Operate and troubleshoot K8s clusters, and understand how physical resources are abstracted up the stack to K8s. GPU platforms (NVIDIA and AMD). Practical experience with GPU drivers and GPU logs investigation tools, e.g. nvidia-smi. Performance diagnostics using NCCL on large scale clusters. Observability

network layers in production. Kubernetes. Operate and troubleshoot K8s clusters, and understand how physical resources are abstracted up the stack to K8s. GPU platforms (NVIDIA and AMD). Practical experience with GPU drivers and GPU logs investigation tools, e.g. nvidia-smi. Performance diagnostics using NCCL on large scale clusters. Observability

network layers in production. Kubernetes. Operate and troubleshoot K8s clusters, and understand how physical resources are abstracted up the stack to K8s. GPU platforms (NVIDIA and AMD). Practical experience with GPU drivers and GPU logs investigation tools, e.g. nvidia-smi. Performance diagnostics using NCCL on large scale clusters. Observability

network layers in production. Kubernetes. Operate and troubleshoot K8s clusters, and understand how physical resources are abstracted up the stack to K8s. GPU platforms (NVIDIA and AMD). Practical experience with GPU drivers and GPU logs investigation tools, e.g. nvidia-smi. Performance diagnostics using NCCL on large scale clusters. Observability

network layers in production. Kubernetes. Operate and troubleshoot K8s clusters, and understand how physical resources are abstracted up the stack to K8s. GPU platforms (NVIDIA and AMD). Practical experience with GPU drivers and GPU logs investigation tools, e.g. nvidia-smi. Performance diagnostics using NCCL on large scale clusters. Observability

network layers in production. Kubernetes. Operate and troubleshoot K8s clusters, and understand how physical resources are abstracted up the stack to K8s. GPU platforms (NVIDIA and AMD). Practical experience with GPU drivers and GPU logs investigation tools, e.g. nvidia-smi. Performance diagnostics using NCCL on large scale clusters. Observability