21 of 21 Remote CUDA Jobs in the UK

OpenGL, Vulkan or shaders); CI experience (CMake, JIRA, Git, Jenkins); GIS development tools (GDAL API, MapBox API); multithreading/parallel computing (GPU programming or CUDA); MATLAB/Python scripting for mathematical/geology problems would be advantageous. Due to specific requirements, applicants without the relevant project experiences will not More ❯

OpenGL, Vulkan or shaders); CI experience (CMake, JIRA, Git, Jenkins); GIS development tools (GDAL API, MapBox API); multithreading/parallel computing (GPU programming or CUDA); MATLAB/Python scripting for mathematical/geology problems would be advantageous. Due to specific requirements, applicants without the relevant project experiences will not More ❯

Agile events and activities. Team Technologies used include Python, Conda, Behavior Driven Development (PyTest-BDD, Cucumber), Gherkin, Ubuntu, Docker, Jenkins, Bash, Groovy, C++/CUDA, JIRA, and Github. Work schedule is flexible, but some intersection with team members in different timezones will be required (two regular meetings per week More ❯

definitions Test frameworks and test development (not test-driven development) Microservices architecture/containerisation (development of containerised plugins, with potential to replace existing components) CUDA integration (AI/ML) Development of new applications to meet user expectations within formal constraints, including HMI/GUI/UX experience Familiarity with More ❯

medical device development Technical Expertise: Experience with multi-tasking systems (real-time preferable) and familiarity with signal processing or AI/ML applications using CUDA on GPUs (preferred), medical device communications protocols (HL7, FHIR) Development Approach: Knowledge of agile methodologies and best practices in software development Tools & Practices: Proficiency More ❯

development Machine-to-machine networking, working to third-party interface definitions Test frameworks and test development (not test-driven development) Microservices architecture/containerisation CUDA integration (AI/ML) Development of new applications to meet user expectations and within formal constraints. HMI/GUI/UX experience needed. Familiarity More ❯

not test-driven development) Microservices architecture/containerisation (needs funding but we can develop containerised plugins going forward and replace existing in slow-time) CUDA integration (AI/ML) Development of new applications to meet user expectations and within formal constraints. HMI/GUI/UX experience needed. Familiarity More ❯

complex machine learning algorithms into scalable, production-quality code, with proficiency in Python and a strong understanding of optimization techniques (experience with Cython and CUDA is a plus). Experience in developing Large Language Models (LLMs) is advantageous. In-depth understanding of computer architecture and its implications on AI More ❯

ML frameworks. Experience optimizing deep learning performance on accelerator hardware. Solid knowledge of deep learning algorithms and compute patterns. Strong programming skills in C++, CUDA, or OpenCL. Background in performance profiling and optimization. BS/MS in Computer Science, Electrical Engineering, or a related field. Interested? Send your CV More ❯

Background: Experience in highly regulated industries, preferably in medical device development. Technical Expertise: Experience with multi-tasking systems, Linux and RTOS, FPGAs, micro-controllers, CUDA, communication protocols (e.g. I2C, SPI, UART, USB, Ethernet, PCIe), driver development and familiarity with signal processing using GPU (preferred). Development Approach: Knowledge of More ❯

AI/ML in the sports domain to create insights or data. Advanced systems knowledge, such as: Developing GPU kernels or ML compilers (e.g., CUDA, OpenCL, TensorRT Plugins, MLIR, TVM ). System optimization for latency and utilization , using tools like Nvidia NSight . Working with embedded SoCs (e.g., Nvidia More ❯

AI/ML in the sports domain to create insights or data. Advanced systems knowledge, such as: Developing GPU kernels or ML compilers (e.g., CUDA, OpenCL, TensorRT Plugins, MLIR, TVM ). System optimization for latency and utilization , using tools like Nvidia NSight . Working with embedded SoCs (e.g., Nvidia More ❯

the boundaries of model performance. You'll also work on re-implementing models in an efficient manner by using PyTorch and underlying technologies like Cuda Kernels, Torch compilation techniques. This would include: Evaluating and optimising compute resource usage (e.g., Hopper GPUs) for cost and time efficiency at training and More ❯

you: 3+ years' experience in GPU development Experience developing GPU/AI Kernels Experience engineering for AI/ML workloads Experience with any of: CUDA, OpenCL, HIP, SYCL Knowledge of deep learning algorithms Interested in optimising tough linear algebra equations Knowledge of AI framework internals (PyTorch, TensorFlow, ONNX etc More ❯

the boundaries of model performance. You'll also work on re-implementing models in an efficient manner by using PyTorch and underlying technologies like Cuda Kernels, Torch compilation techniques. This would include: Evaluating and optimising compute resource usage (e.g., Hopper GPUs) for cost and time efficiency at training and More ❯

Sports tech experience: Background applying AI/ML in the sports domain for data generation or insights. Systems optimisation: Knowledge of GPU kernel development (CUDA, OpenCL, etc.), real-time system optimisation (e.g., Nvidia NSight), or experience working with embedded SoCs (Nvidia, Qualcomm, etc.). If you're interested in More ❯

Hands-on with monitoring tools like Prometheus and Grafana Nice to have: Experience building Developer Experience (DevX) tools and workflows Familiarity with GPU setups (CUDA, TensorFlow, etc.) Strong networking and network security knowledge Linux/Unix skills and shell scripting A degree in Computer Science or a related field More ❯

Hands-on with monitoring tools like Prometheus and Grafana Nice to have: Experience building Developer Experience (DevX) tools and workflows Familiarity with GPU setups (CUDA, TensorFlow, etc.) Strong networking and network security knowledge Linux/Unix skills and shell scripting A degree in Computer Science or a related field More ❯

Expertise in Dealing with data. Knowledge of machine learning models and architectures, including supervised learning. Experience with GPU training and supporting libraries such as CUDA and PyTorch. Proficiency in the broader data science ecosystem, including pandas and numpy. Familiarity with Kafka data streaming and multiprocessing for deploying models. Strong More ❯

to hear from you.) 💡 The Stack & Environment: A diverse, modern environment spanning: Linux, Windows, MacOS, Microsoft 365, Azure AD, Intune, Teams, NICE DCV, Nvidia CUDA, Slurm, Jira Service Desk, Terraform, Azure Resource Manager 💡 What We’re Looking For: 2+ years of experience administering HPC infrastructure Hands-on experience with … Infiniband, Slurm, and GPU compute platforms (e.g. CUDA) Proficiency in systems administration and troubleshooting Strong documentation habits and a customer-focused mindset Experience with VDI solutions and monitoring tools 💡 Bonus Points: Familiarity with Jira Service Desk and Terraform scripting Exposure to SSL management, infrastructure-as-code, or cloud database More ❯

to hear from you.) 💡 The Stack & Environment: A diverse, modern environment spanning: Linux, Windows, MacOS, Microsoft 365, Azure AD, Intune, Teams, NICE DCV, Nvidia CUDA, Slurm, Jira Service Desk, Terraform, Azure Resource Manager 💡 What We’re Looking For: 2+ years of experience administering HPC infrastructure Hands-on experience with … Infiniband, Slurm, and GPU compute platforms (e.g. CUDA) Proficiency in systems administration and troubleshooting Strong documentation habits and a customer-focused mindset Experience with VDI solutions and monitoring tools 💡 Bonus Points: Familiarity with Jira Service Desk and Terraform scripting Exposure to SSL management, infrastructure-as-code, or cloud database More ❯