15 of 15 CUDA Jobs in 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 ❯

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 ❯

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 ❯

with a bias for clean abstractions, reliability, and performance Bonus points for: Experience deploying open-source LLMs or VLMs in production Experience with ML inference systems, PyTorch, Triton, or CUDA kernels Background in document intelligence, enterprise search, or NLP pipelines Prior exposure to multi-agent systems or complex orchestration workflows This is a high-impact, high-autonomy role in More ❯

with a bias for clean abstractions, reliability, and performance Bonus points for: Experience deploying open-source LLMs or VLMs in production Experience with ML inference systems, PyTorch, Triton, or CUDA kernels Background in document intelligence, enterprise search, or NLP pipelines Prior exposure to multi-agent systems or complex orchestration workflows This is a high-impact, high-autonomy role in 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 ❯

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 ❯

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 ❯

optimise state-of-the-art algorithms and architectures, ensuring compute efficiency and performance. Low-Level Mastery: Write high-quality Python, C/C++, XLA, Pallas, Triton, and/or CUDA code to achieve performance breakthroughs. Required Skills Understanding of Linux systems, performance analysis tools, and hardware optimisation techniques Experience with distributed training frameworks (Ray, Dask, PyTorch Lightning, etc.) Expertise … with machine learning frameworks (JAX, Tensorflow, PyTorch etc.) Passion for profiling, identifying bottlenecks, and delivering efficient solutions. Highly Desirable Track record of successfully scaling ML models. Experience writing custom CUDA kernels or XLA operations. Understanding of GPU/TPU architectures and their implications for efficient ML systems. Fundamentals of modern Deep Learning Actively following ML trends and a desire … to push boundaries. Example Projects: Profile algorithm traces, identifying opportunities for custom XLA operations and CUDA kernel development. Implement and apply SOTA architectures (MAMBA, Griffin, Hyena) to research and applied projects. Adapt algorithms for large-scale distributed architectures across HPC clusters. Employ memory-efficient techniques within models for increased parameter counts and longer context lengths. What We Offer: Real More ❯

to deploy GPU and ML workloads at scale. Provision and optimise GPU cloud infrastructure (AWS, GCP, Azure) using Terraform/Ansible. Collaborate with GPU engineers and researchers to integrate CUDA, SYCL, Vulkan, and ML kernels into production workflows. Support secure packaging, deployment, and distribution of GPU-accelerated software to partners and clients. Evolve infrastructure to support hybrid AI/… GitLab CI, etc.). Proficiency in containerisation and orchestration (Docker, Kubernetes). Experience with cloud GPU infrastructure (AWS, Azure, GCP) and IaC (Terraform, Ansible). Familiarity with GPU workflows (CUDA, SYCL, Vulkan, OpenCL) or HPC performance optimisation. Strong scripting and programming skills (Python, Bash, C/C++ exposure a plus). Knowledge of monitoring, logging, and performance testing for 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 ❯