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 ❯
Cambridge, Cambridgeshire, United Kingdom Hybrid / WFH Options
Arm Limited
Good oral and written English skills "Nice To Have" Skills and Experience : Experience with ML software frameworks (e.g. PyTorch) Familiarity with ML hardware accelerators (e.g. NPUs, TPUs, GPUs with CUDA support) Knowledge of optimising and profiling software Experience with assembly programming Software development and integration on Linux, Android, or similar systems Knowledge of scripting languages, including Python In Return More ❯
large language models, efficient computing based on low-precision arithmetic, deep learning models including large generative models for language, vision and other modalities . Experience writing C Triton/CUDA kernels for performance optimisation of ML models. Have contributed to open-source projects or published research papers in relevant fields. Knowledge of cloud computing platforms. Keen to present, publish More ❯
Familiarity with a broad range of ML techniques (computer vision, generative models, audio processing, etc.), or the ability to adapt quickly to new areas. Exposure to GPU programming (e.g., CUDA) is a plus. Ability to communicate complex technical ideas clearly to colleagues and stakeholders. Client-facing experience or a willingness to engage with stakeholders and project teams is highly More ❯
deploying machine learning onto a range of hardware from resource constrained embedded systems through to edge computing is desirable. As is any knowledge of GPU programming languages and frameworks (CUDA, ROCm, etc). Your future colleagues will be similarly highly skilled, with experience across industry and the drive to innovate. You will find yourself in a low-management work More ❯
deploying machine learning onto a range of hardware from resource constrained embedded systems through to edge computing is desirable. As is any knowledge of GPU programming languages and frameworks (CUDA, ROCm, etc). Your future colleagues will be similarly highly skilled, with experience across industry and the drive to innovate. You will find yourself in a low-management work More ❯
