5 of 5 CUDA Jobs in the City of London

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 ...

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 ...

Alertmanager. Experience deploying and operating large-scale GPU clusters or HPC systems (Ideally). 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. ...

workflows (Unreal, Unity) and production asset pipelines Familiarity with CI/CD for desktop applications Nice to have: Experience with parallel-computing frameworks (OpenMP, CUDA) Knowledge of advanced rendering techniques (ray tracing, global illumination) Experience with computer-vision libraries (OpenCV) Tech Stack: Languages: C++, Python Graphics: OpenGL, GLSL … Tools: Blender, Unreal Formats: Alembic, USD, glTF/GLB, OpenImageIO, OpenEXR Optimization: OpenMP, CUDA IPC: ZeroMQ (cppzmq/pyzmq) Build: CMake, Conda, Git, CI/CD Platforms: Windows (primary), Linux Senior Software Engineer | Computer Vision | C++ | Python | 3D | OpenGL | Hybrid, London ...

/Machine Learning/C++/Python/CUDA Machine Learning Engineer – AI & Foundation Models (Future CTO Track) West London | 3–4 days onsite Up to £150k + equity Company: AI StartUp We’re hiring one of the first technical engineers for a pioneering AI startup building a foundation … with the founders and evolving toward Head of Engineering/CTO. You’ll work on: High-performance ML systems (training + inference) C++/CUDA optimisation and GPU-accelerated components Large models (e.g. transformers), latency & throughput optimisation Distributed systems and cloud deployment Internal tooling, benchmarks, and evaluation frameworks Ideal ...