6 of 6 CUDA Jobs in the City of London

PyTorch or TensorFlow for AI-based perception or control. Exposure to distributed, embedded, or real-time systems. Knowledge of simulation tools like Gazebo or RViz. Experience with GPU programming (CUDA/OpenCL) or control optimisation. Familiar with robotics frameworks – MoveIt, Eigen, OpenCV. Understanding of sensor fusion, mapping, and SLAM pipelines. Why You’ll Like It: Early-stage company that 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 ❯

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 ❯

field of HPC to join the firm, and partner with academic and industrial partners to keep their systems at the cutting edge of compute. Stack: C++, Python, Linux, CPU, CUDA The firm can offer industry leading salaries and the opportunity to partner with London's best researchers and technologists. Please do apply 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 ❯