9 of 9 Permanent CUDA Jobs in London

design patterns. Experience in data science tools and ML tools (e.g., NumPy, pandas, scikit-learn, PyTorch) and open-source contributions (especially Python-based) would be a bonus. Familiarity with CUDA, GPU-based computations, end-to-end neural network training, MLOps, and academic research in machine learning are also beneficial. Experience configuring and maintaining cloud infrastructure including network infrastructure, compute More ❯

design patterns. Experience in data science tools and ML tools (e.g., NumPy, pandas, scikit-learn, PyTorch) and open-source contributions (especially Python-based) would be a bonus. Familiarity with CUDA, GPU-based computations, end-to-end neural network training, MLOps, and academic research in machine learning are also beneficial. Experience configuring and maintaining cloud infrastructure including network infrastructure, compute More ❯

or related field, or equivalent experience in 3D graphics or ML systems. Strong in C Python, 3D graphics pipelines, inverse rendering, and geometry processing. Experience with GPU programming (OpenGL, CUDA, Vulkan), PyTorch/TensorFlow, and real-time engines (Unity/Unreal). Solid grasp of optimization, linear algebra, computer vision, and numerical methods. Skilled in multithreading, memory management, and More ❯

or related field, or equivalent experience in 3D graphics or ML systems. Strong in C Python, 3D graphics pipelines, inverse rendering, and geometry processing. Experience with GPU programming (OpenGL, CUDA, Vulkan), PyTorch/TensorFlow, and real-time engines (Unity/Unreal). Solid grasp of optimization, linear algebra, computer vision, and numerical methods. Skilled in multithreading, memory management, and More ❯

configuration management tools (Ansible, Puppet, or similar). Experience deploying and operating large-scale GPU clusters or HPC systems. Working knowledge of ML infrastructure and familiarity with GPU drivers, CUDA, and container runtimes. A low-ego, collaborative approach and a clear, proactive communication style. In short: This is a role for engineers who like big systems, hard problems, and More ❯

configuration management tools (Ansible, Puppet, or similar). Experience deploying and operating large-scale GPU clusters or HPC systems. Working knowledge of ML infrastructure and familiarity with GPU drivers, CUDA, and container runtimes. A low-ego, collaborative approach and a clear, proactive communication style. In short: This is a role for engineers who like big systems, hard problems, and More ❯

architectures Develop low-latency inference systems providing real-time, high-accuracy predictions in production Optimise and extend machine learning frameworks to improve training and inference performance Leverage GPU programming (CUDA, cuDNN, TensorRT) to maximise efficiency Automate model experimentation, tuning and retraining in partnership with research teams Work with infrastructure specialists to optimise workflows and reduce compute costs Assess and … ML development and deployment Skills and Experience 5+ years’ experience in machine learning with a focus on training and inference systems Strong programming expertise in Python and C++ or CUDA Proficiency with PyTorch, TensorFlow or JAX Hands-on experience with GPU acceleration and distributed training (Horovod, NCCL or similar) Background in real-time, low-latency ML pipelines Familiarity with More ❯

architectures Develop low-latency inference systems providing real-time, high-accuracy predictions in production Optimise and extend machine learning frameworks to improve training and inference performance Leverage GPU programming (CUDA, cuDNN, TensorRT) to maximise efficiency Automate model experimentation, tuning and retraining in partnership with research teams Work with infrastructure specialists to optimise workflows and reduce compute costs Assess and … ML development and deployment Skills and Experience 5+ years’ experience in machine learning with a focus on training and inference systems Strong programming expertise in Python and C++ or CUDA Proficiency with PyTorch, TensorFlow or JAX Hands-on experience with GPU acceleration and distributed training (Horovod, NCCL or similar) Background in real-time, low-latency ML pipelines Familiarity with More ❯

functions in C ATen. Build and validate Python bindings with correct gradient propagation and test coverage. Create "golden" reference implementations in eager mode for correctness validation. Collaborate asynchronously with CUDA or systems engineers who handle low-level kernel optimization. Profile, benchmark, and report performance trends at the operator and graph level. Document assumptions, APIs, and performance metrics for reproducibility. … plus. 4) More About the Opportunity Ideal for contractors who enjoy building clean, high-performance abstractions in deep learning frameworks. Work is asynchronous, flexible, and outcome-oriented. Collaborate with CUDA optimization specialists to integrate and validate kernels. Projects may involve primitives used in state-of-the-art AI models and benchmarks. 5) Compensation & Contract Terms Typical range More ❯