1 to 25 of 53 CUDA Jobs in the UK

organisational skills Additional skills in any of the following also beneficial C#, WinForms, WPF, Qt/QML framework, HTML5, experience with algorithmic problems (OpenCL, CUDA), Machine learning and AI, OpenGL, OpenGL Shaders, VTK, OSG, Vulkan, JIRA, TestRail, TFS, Git, Jenkins, csh/bash, batch files, cmake, PowerShell. About the More ❯

organisational skills Additional skills in any of the following also beneficial C#, WinForms, WPF, Qt/QML framework, HTML5, experience with algorithmic problems (OpenCL, CUDA), Machine learning and AI, OpenGL, OpenGL Shaders, VTK, OSG, Vulkan, JIRA, TestRail, TFS, Git, Jenkins, csh/bash, batch files, cmake, PowerShell. About the More ❯

or Fortran). - Further experience with C# (including WinForms, WPF, QML Frameworks) would be desirable. - Strong numeracy and understanding of mathematical modelling. - Experience with CUDA, multithreading, AI/ML would be advantageous. - Any exposure to 3D/2D graphics development (OpenGL, Vulkan) would be beneficial. The company offers an More ❯

systems. Familiarity with SOLID principles and modern software design patterns. Experience with Docker and build pipelines. Proficient in version control using Git. Experience with CUDA for GPU programming. Solid understanding of Linux systems. Additional Technical Skills (Desired): Python, QT, and QML development. Familiarity with Atlassian Jira and Confluence. Experience 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 ❯

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 ❯

another engineering field. Examples include nonlinear estimation, numerical simulation, nonlinear optimization, and control theory. Experience in the following would be beneficial but not mandatory: CUDA C/C++ GPU computing High performance computing Scientific computing Natural language processing Computer vision Compensation and Benefits: Base Salary Range More ❯

MLIR, Triton, etc.). Expertise in tailoring algorithms and ML models to exploit GPU strengths and minimize weaknesses. Knowledge of low-level GPU programming (CUDA, OpenCL, etc.) and performance tuning techniques. Understanding of modern GPU architectures, memory hierarchies, and performance bottlenecks. Ability to develop and utilize sophisticated performance models 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 ❯

maintainable solutions. PREFERRED EXPERIENCE: GPU Kernel Development & Optimization: Proficient experienced in designing and optimizing GPU kernels for deep learning on AMD GPUs using HIP, CUDA, and assembly (ASM). Strong knowledge of AMD architectures (GCN, RDNA) and low-level programming to maximize performance for AI operations, leveraging tools like 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 ❯

a strong focus on memory management, multi-threading, and low-level performance optimizations. Experience with GPU architectures (e.g., NVIDIA, AMD) and programming frameworks like CUDA, OpenCL, and TensorFlow. Understanding of machine learning algorithms, including model training and inference, and how to optimize these for GPU-based computation. Strong knowledge More ❯

a strong focus on memory management, multi-threading, and low-level performance optimizations. Experience with GPU architectures (e.g., NVIDIA, AMD) and programming frameworks like CUDA, OpenCL, and TensorFlow. Understanding of machine learning algorithms, including model training and inference, and how to optimize these for GPU-based computation. Strong knowledge More ❯

a strong focus on memory management, multi-threading, and low-level performance optimizations. Experience with GPU architectures (e.g., NVIDIA, AMD) and programming frameworks like CUDA, OpenCL, and TensorFlow. Understanding of machine learning algorithms, including model training and inference, and how to optimize these for GPU-based computation. Strong knowledge More ❯

Kernel, Hardware Accelerator, TVM, or Code-gen • Experience with Deep Learning: C++ or Python, and AI, Neural Network, Tensorflow, PyTorch, MxNET, Llvm, Compiler, CPU, CUDA, Nvidia, TensorRT, TPU, Cluster Management, High Performance Computing, or Optimization Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce 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 ❯

and/or academic), particularly Neural Networks Programming in a commercial environment, from design through to deployment Programming/scripting languages: Python, C, C++, CUDA, Bash, working in a Linux environment Low-level code optimisation, benchmarking and building test suites API design, development and maintenance Product release management Deployment 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 ❯

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 ❯