1 to 25 of 50 Permanent CUDA Jobs in London

in Agile events and activities. Team Technologies used include Python, Conda, Behavior Driven Development (PyTest-BDD, Cucumber), Gherkin, Ubuntu, Docker, Jenkins, Bash, Groovy, C CUDA, JIRA, and Github. Work schedule is flexible, but some intersection with team members in different timezones will be required (two regular meetings per week More ❯

MLIR, Triton, etc.). Expertise in tailoring algorithms and ML models to exploit GPU strengths and minimize weaknesses. Knowledge of low-level GPU programming (CUDA, OpenCL, etc.) and performance tuning techniques. Understanding of modern GPU architectures, memory hierarchies, and performance bottlenecks. Ability to develop and utilize sophisticated performance models More ❯

medical device development Technical Expertise: Experience with multi-tasking systems (real-time preferable) and familiarity with signal processing or AI/ML applications using CUDA on GPUs (preferred), medical device communications protocols (HL7, FHIR) Development Approach: Knowledge of agile methodologies and best practices in software development Tools & Practices: Proficiency More ❯

C++ The nice-to-haves PhD in computer vision or robotics Experience with machine learning techniques for geometric and semantic estimation GPU programming skills (CUDA, OpenCL, Vulkan, Metal) Experience with embedded software development All about us We're a London-based startup founded by visual SLAM algorithm pioneers. Having More ❯

complex machine learning algorithms into scalable, production-quality code, with proficiency in Python and a strong understanding of optimization techniques (experience with Cython and CUDA is a plus). Experience in developing Large Language Models (LLMs) is advantageous. In-depth understanding of computer architecture and its implications on AI More ❯

and collaboration skills to work effectively in multidisciplinary teams.• Knowledge of ethical AI practices and laws is a plus. Preferred Skills: Knowledge of NVIDIA CUDA, cuDNN, TensorRT and Experience with NVIDIA GPU hardware and software stack Understanding of HPC and AI workloads. Familiarity with BigData platforms and technologies, such More ❯

and collaboration skills to work effectively in multidisciplinary teams.• Knowledge of ethical AI practices and laws is a plus. Preferred Skills: Knowledge of NVIDIA CUDA, cuDNN, TensorRT and Experience with NVIDIA GPU hardware and software stack Understanding of HPC and AI workloads. Familiarity with BigData platforms and technologies, such More ❯

a strong focus on memory management, multi-threading, and low-level performance optimizations. Experience with GPU architectures (e.g., NVIDIA, AMD) and programming frameworks like CUDA, OpenCL, and TensorFlow. Understanding of machine learning algorithms, including model training and inference, and how to optimize these for GPU-based computation. Strong knowledge More ❯

to 5 years' experience building technical or scientific software Bonus if you have understanding of: CFD, meshing, parallel computing, GPU's such as CUDA, and or CAD More ❯

to 5 years' experience building technical or scientific software Bonus if you have understanding of: CFD, meshing, parallel computing, GPU's such as CUDA, and or CAD More ❯

ML frameworks. Experience optimizing deep learning performance on accelerator hardware. Solid knowledge of deep learning algorithms and compute patterns. Strong programming skills in C++, CUDA, or OpenCL. Background in performance profiling and optimization. BS/MS in Computer Science, Electrical Engineering, or a related field. Interested? Send your CV More ❯

Background: Experience in highly regulated industries, preferably in medical device development. Technical Expertise: Experience with multi-tasking systems, Linux and RTOS, FPGAs, micro-controllers, CUDA, communication protocols (e.g. I2C, SPI, UART, USB, Ethernet, PCIe), driver development and familiarity with signal processing using GPU (preferred). Development Approach: Knowledge of More ❯

AI/ML in the sports domain to create insights or data. Advanced systems knowledge, such as: Developing GPU kernels or ML compilers (e.g., CUDA, OpenCL, TensorRT Plugins, MLIR, TVM ). System optimization for latency and utilization , using tools like Nvidia NSight . Working with embedded SoCs (e.g., Nvidia More ❯

solutions. Proficiency in C++. Desirable experience: PhD in computer vision or robotics. Experience with machine learning techniques for geometric & semantic estimation. GPU programming skills (CUDA, OpenCL, Vulkan, Metal). Experience with embedded software development. If this role is of any interest please apply directly on LinkedIn or send a More ❯

solutions. Proficiency in C++. Desirable experience: PhD in computer vision or robotics. Experience with machine learning techniques for geometric & semantic estimation. GPU programming skills (CUDA, OpenCL, Vulkan, Metal). Experience with embedded software development. If this role is of any interest please apply directly on LinkedIn or send a More ❯

Tech Stack Our client is tech-agnostic and values adaptability. Current tools include: Backend : Python Frontend : TypeScript, React Infrastructure : Kubernetes, GCP Machine Learning : PyTorch, CUDA, Ray What’s on Offer Highly competitive base salary + commission + equity in a hyper-growth company 25 days holiday + public holidays More ❯

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 More ❯

detailed breakdown of all the technologies we use: Backend: Python Frontend: Typescript and React Kubernetes for deployment GCP for underlying infrastructure Machine Learning: PyTorch, CUDA, Ray We encourage people from all backgrounds, cultures, and skill levels to apply. It is okay to not meet all requirements listed as we More ❯

Sports tech experience: Background applying AI/ML in the sports domain for data generation or insights. Systems optimisation: Knowledge of GPU kernel development (CUDA, OpenCL, etc.), real-time system optimisation (e.g., Nvidia NSight), or experience working with embedded SoCs (Nvidia, Qualcomm, etc.). If you're interested in More ❯

Sports tech experience: Background applying AI/ML in the sports domain for data generation or insights. Systems optimisation: Knowledge of GPU kernel development (CUDA, OpenCL, etc.), real-time system optimisation (e.g., Nvidia NSight), or experience working with embedded SoCs (Nvidia, Qualcomm, etc.). If you're interested in More ❯

the boundaries of model performance. You'll also work on re-implementing models in an efficient manner by using PyTorch and underlying technologies like Cuda Kernels, Torch compilation techniques. This would include: Evaluating and optimising compute resource usage (e.g., Hopper GPUs) for cost and time efficiency at training and More ❯

e.g., DDP, FSDP) and model performance optimization • Experience with GPU architectures, cloud platforms, and hardware tradeoffs • Familiarity with low-level hardware tuning and custom CUDA kernel development Key Responsibilities • Develop cutting-edge AI models for drug discovery using deep learning frameworks • Scale and optimize distributed training of large AI More ❯

e.g., DDP, FSDP) and model performance optimization • Experience with GPU architectures, cloud platforms, and hardware tradeoffs • Familiarity with low-level hardware tuning and custom CUDA kernel development Key Responsibilities • Develop cutting-edge AI models for drug discovery using deep learning frameworks • Scale and optimize distributed training of large AI More ❯

e.g., DDP, FSDP) and model performance optimization • Experience with GPU architectures, cloud platforms, and hardware tradeoffs • Familiarity with low-level hardware tuning and custom CUDA kernel development Key Responsibilities • Develop cutting-edge AI models for drug discovery using deep learning frameworks • Scale and optimize distributed training of large AI More ❯

Hands-on with monitoring tools like Prometheus and Grafana Nice to have: Experience building Developer Experience (DevX) tools and workflows Familiarity with GPU setups (CUDA, TensorFlow, etc.) Strong networking and network security knowledge Linux/Unix skills and shell scripting A degree in Computer Science or a related field More ❯