1 to 25 of 67 Permanent CUDA Jobs in England

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 is a plus. Experience with Rust is a plus. Familiarity with embedded systems and real-time operating systems is a plus. Familiarity with CUDA for GPU programming and experience with efficiency optimizations is a plus. Analytical Skills: Strong analytical and problem-solving skills with a focus on delivering 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 ❯

in 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 ❯

Geant4) Multi-physics modelling software (e.g. MOOSE, OpenFOAM) High-performance computing systems; Debugging and profiling tools; Parallel/GPU programming techniques (e.g. MPI, OpenMP, CUDA, HIP, Kokkos, Sycl) Proven ability to disseminate research via presentations or publications, with familiarity in modern software engineering practices. Additional Information A full list More ❯

Geant4) - Multi-physics modelling software (e.g. MOOSE, OpenFOAM) - High-performance computing systems; Debugging and profiling tools; Parallel/GPU programming techniques (e.g. MPI, OpenMP, CUDA, HIP, Kokkos, Sycl) - Proven ability to disseminate research via presentations or publications, with familiarity in modern software engineering practices. Additional Information For a full More ❯

Geant4) - Multi-physics modelling software (e.g. MOOSE, OpenFOAM) - High-performance computing systems; Debugging and profiling tools; Parallel/GPU programming techniques (e.g. MPI, OpenMP, CUDA, HIP, Kokkos, Sycl) - Proven ability to disseminate research via presentations or publications, with familiarity in modern software engineering practices. Additional Information For a full 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 ❯

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 ❯

C++ The nice-to-haves PhD in computer vision or robotics Experience with machine learning techniques for geometric and semantic estimation GPU programming skills (CUDA, OpenCL, Vulkan, Metal) Experience with embedded software development All about us We're a London-based startup founded by visual SLAM algorithm pioneers. Having 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 ❯

and collaboration skills to work effectively in multidisciplinary teams.• Knowledge of ethical AI practices and laws is a plus. Preferred Skills: Knowledge of NVIDIA CUDA, cuDNN, TensorRT and Experience with NVIDIA GPU hardware and software stack Understanding of HPC and AI workloads. Familiarity with BigData platforms and technologies, such More ❯

and collaboration skills to work effectively in multidisciplinary teams.• Knowledge of ethical AI practices and laws is a plus. Preferred Skills: Knowledge of NVIDIA CUDA, cuDNN, TensorRT and Experience with NVIDIA GPU hardware and software stack Understanding of HPC and AI workloads. Familiarity with BigData platforms and technologies, such 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 ❯

to 5 years' experience building technical or scientific software Bonus if you have understanding of: CFD, meshing, parallel computing, GPU's such as CUDA, and or CAD More ❯

to 5 years' experience building technical or scientific software Bonus if you have understanding of: CFD, meshing, parallel computing, GPU's such as CUDA, and or CAD 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 ❯