1 to 25 of 150 CUDA Jobs in England

Strong mathematical and analytical problem-solving skills. Desirable Skills: Experience with C# .NET, WinForms, WPF, or the Qt/QML framework, or HTML5. Experience in GPU programming (e.g., OpenCL, CUDA). Knowledge of AI and Machine Learning techniques. Expertise in graphics development (2D/3D) using technologies such as OpenGL, OpenGL Shaders, VTK, OSG, or Vulkan. Why apply for More ❯

in C++ (and ideally C 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 excellent remunerations package including annual More ❯

in C++ (and ideally C 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 excellent remunerations package including annual More ❯

Qt, QML); 3D graphics toolkits (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 be considered (similarly exposure to More ❯

Qt, QML); 3D graphics toolkits (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 be considered (similarly exposure to More ❯

tools (e.g. OpenMC, MCNP, Fluka, 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 list of benefits and to More ❯

written and verbal communication skills, work well within a multinational team and with multinational customers. Excellent cultural awareness is essential. Experience developing firmware or drivers for GPUs. Knowledge of CUDA or OpenCL Experience working on upstreaming of kernel code/contributing to Linux kernel. Exposure to integration with data centre/cloud service operations - for example integration with Kubernetes More ❯

/OSU, TensorFlow, PyTorch, MLperf. 1+ years of experience of using parallel and/or accelerated applications using more than one of OneAPI, Intel Gaudi software suite, OpenMP, MPI, CUDA, ROCm, OpenCL, SYCL. We will also consider candidates who lack some of the requirements but can demonstrate ability to learn quickly. The following competencies are welcome: Experience of running … performance tests and benchmarking codes. Experience of using or developing parallel and/or accelerated applications using more than one of OpenMP, MPI, CUDA, ROCm, OpenCL, SYCL paradigms. Experience of production HPC environment: large-scale filesystems (ideally Storage Scale), batch scheduling (ideally SLURM) as well as common HPC SW and management tools. Experience with analysis and profiling tools for More ❯

systems for GPU architectures (OpenXLA, 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 and benchmarks to guide optimization More ❯

software on GitHub, PyPI, Anaconda Cloud, and Docker Hub, as well as use of Pytorch lightning, Git, test-driven design. Knowledge of parallel computing technologies, such as NVIDIA's CUDA platform, OpenCL, and OpenMPI. The salary range for Cambridge, UK: - Senior Scientist I, Computational Biology: £75,000 - £117,500 Senior Scientist II, Computational Biology: £94,000 - £152,500 Exact More ❯

working to third-party interface 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 embedded development tools and approaches More ❯

highly regulated industries, preferably in 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 with version control systems (e.g. More ❯

at a leading technology company. Strong expertise in algorithms, data structures, multivariate calculus, and linear algebra. Proficient in Python, TensorFlow, PyTorch, or similar languages and frameworks, with experience writing CUDA kernels and profiling GPU code a plus. Excellent communication skills, with the ability to work effectively in cross-functional teams and present complex ideas to both technical and non More ❯

at a leading technology company. Strong expertise in algorithms, data structures, multivariate calculus, and linear algebra. Proficient in Python, TensorFlow, PyTorch, or similar languages and frameworks, with experience writing CUDA kernels and profiling GPU code a plus. Excellent communication skills, with the ability to work effectively in cross-functional teams and present complex ideas to both technical and non More ❯

looking for: Essential: C# software 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 with the tools and approaches More ❯

of the mathematical foundations of deep learning, including multivariate calculus, linear algebra, and optimization techniques. Proficient in Python and deep learning frameworks such as TensorFlow and PyTorch. Experience with CUDA kernels and GPU profiling is a plus. Excellent communication skills, with the ability to present complex technical ideas to both technical and non-technical audiences. Knowledge of quantitative finance More ❯

of the mathematical foundations of deep learning, including multivariate calculus, linear algebra, and optimization techniques. Proficient in Python and deep learning frameworks such as TensorFlow and PyTorch. Experience with CUDA kernels and GPU profiling is a plus. Excellent communication skills, with the ability to present complex technical ideas to both technical and non-technical audiences. Knowledge of quantitative finance More ❯

engineering principles to ensure robust, 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 Compute Kernel (CK), CUTLASS, and More ❯

language, vision and other modalities, machine learning for molecules and proteins (ideally with some background in chemistry and biological sciences) . Lower-level programming for hardware efficiency, e.g. C CUDA/Triton. Practical familiarity with hardware capabilities for deep learning - threads, caches, vector & matrix engines, data dependencies, bus widths and throttling. Practical familiarity with software stacks for deep learning More ❯

language, vision and other modalities, machine learning for molecules and proteins (ideally with some background in chemistry and biological sciences). Lower-level programming for hardware efficiency, e.g. C CUDA/Triton. Practical familiarity with hardware capabilities for deep learning - threads, caches, vector & matrix engines, data dependencies, bus widths and throttling. Practical familiarity with software stacks for deep learning More ❯

industry experience. Expertise in translating 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/ML performance. Comprehensive knowledge More ❯

Experience: Proficiency in C++ with 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 of parallel computing, vectorization, and More ❯

Experience: Proficiency in C++ with 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 of parallel computing, vectorization, and More ❯

approach to AI implementation. Effective communication and collaboration skills in cross-functional teams . Preferred Skills High-Performance Computing (HPC) and AI workloads for large-scale enterprise solutions. NVIDIA CUDA, cuDNN, TensorRT experience for deep learning acceleration. Big Data platforms (Hadoop, Spark) for AI-driven analytics in professional services. Pls share CV at payal.c@hcltech.com More ❯

approach to AI implementation. Effective communication and collaboration skills in cross-functional teams . Preferred Skills High-Performance Computing (HPC) and AI workloads for large-scale enterprise solutions. NVIDIA CUDA, cuDNN, TensorRT experience for deep learning acceleration. Big Data platforms (Hadoop, Spark) for AI-driven analytics in professional services. Pls share CV at payal.c@hcltech.com More ❯