1 to 25 of 139 CUDA Jobs in England

Spark or similar is a plus. Model optimisation and research-to-prototype deployment fundamentals, including ONNX, TorchScript, FastAPI/gRPC and GPU acceleration, with CUDA basics desirable. Responsible AI and security awareness: explainability, privacy-preserving methods, bias assessment, safety, assurance, adversarial robustness and secure AI. Scientific and technical writing ...

communication skills. Desirable experience Vision Transformer architectures. Object detection, tracking, segmentation or pose estimation. Multi-object localisation or multi-modal perception systems. GPU optimisation, CUDA, inference acceleration or edge AI. Real-time computer vision systems. Docker, Kubernetes, Kubeflow or MLOps pipelines. Aerial, satellite or ISR imagery. Synthetic data generation ...

necessary Desirable Exposure to computational research involving GPU, including AI/ML methods applied to biomedical problems Experience supporting GPU‐accelerated workloads, NVIDIA tooling, CUDA‐aware environments, and/or AI/ML and bioinformatics workloads on shared compute platforms. Familiarity with bioinformatics or scientific workflow frameworks (e.g., Nextflow ...

into reliable software. Experience developing maintainable code using version control, testing and CI/CD. Strong analytical, problem-solving and communication skills. Desirable experience CUDA and GPU programming. Python and PyTorch. CNNs, Transformers or broader deep learning architectures. Synthetic training imagery and simulation environments. ISR imagery or defence-related ...

Practical MLOps: experiment tracking and model versioning with MLflow, hyperparameter optimisation, and reproducible, configuration‐driven training runs. Containerisation and infrastructure as code: Docker and CUDA‐based GPU images, plus enough Terraform to ship your own model to an environment through code review rather than a console. Strong software engineering ...

kernel/network tuning, lock-free programming, hardware-aware optimization, or FPGA-adjacent systems. Experience with ML/data tooling such as PyTorch, JAX, CUDA, GPU clusters, Ray, Kafka, Kubernetes, Spark, or comparable technologies. Understanding of market microstructure, backtesting pitfalls, transaction costs, and the operational needs of live quantitative ...

Practical MLOps: experiment tracking and model versioning with MLflow, hyperparameter optimisation, and reproducible, configuration‐driven training runs. Containerisation and infrastructure as code: Docker and CUDA‐based GPU images, plus enough Terraform to ship your own model to an environment through code review rather than a console. Strong software engineering ...

Practical MLOps: experiment tracking and model versioning with MLflow, hyperparameter optimisation, and reproducible, configuration-driven training runs. Containerisation and infrastructure as code: Docker and CUDA-based GPU images, plus enough Terraform to ship your own model to an environment through code review rather than a console. Strong software engineering ...

resilience, and results in a high‐performance startup environment. Passion for AI, open source, and helping customers achieve real outcomes. Nice to have Kubernetes, CUDA, TensorRT, ONNX, Jetson, or other edge accelerators. Experience with MLOps, model monitoring, CI/CD, or observability. Familiarity with AWS, Azure, GCP, serverless systems ...

camera data processing. Experience with IMU/GNSS‐aided sensor fusion. Experience with C++, C# or Python in engineering software. Familiarity with Linux, Docker, CUDA or GPU acceleration. Understanding of navigation, localisation or mapping products. Familiarity with React or web‐based visualisation tools. Experience using AI coding assistants ...

Spark or distributed data processing * Docker, Kubernetes or Cloud Run * Terraform or other infrastructure-as-code technologies * GPU or accelerated computing technologies such as CUDA, CuPy or Numba * Numerical or high-performance computing * Insurance, finance, risk, analytics or other data-intensive environments Why Join? This is an opportunity ...

effectively with trading researchers Reliable and predictable availability required Bonus Points Experience with HPC and distributed large model training Experience with GPU performance optimisation (CUDA or ROCm) Experience with end-to-end model development Strong opinions on best practices in ML research, tooling, and/or infrastructure Benefits include ...

Strata, QuantLib, ORE) is an advantage. Hands‐on HPC experience: Parallel/concurrent programming (multithreading, multiprocessing, synchronization, concurrent data structures). GPU compute with CUDA, including GPU architecture (memory hierarchy, warps, occupancy, host–device transfers); ROCm/HIP a plus. Writing and optimizing compute kernels: profiling (e.g. Nsight/ ...

such as latency, throughput, scalability and cost. Direct development experience with FFmpeg or GStreamer. Experience with GPU-accelerated video or AI technologies such as CUDA, PyTorch, TensorRT, Triton Inference Server or NVIDIA DeepStream. #J-18808-Ljbffr ...

utilisation across both single-GPU and distributed multi-GPU environments . Improving Python and model inference performance using technologies including NumPy, Numba, Triton and CUDA-based libraries . Conducting load and stress testing to ensure AI services remain performant and stable under high levels of concurrent traffic. Optimising cloud ...

pipelines and optimizing/productizing ML models. Proficiency in AI/ML frameworks (e.g. PyTorch) and computer vision libraries (e.g., OpenCV, PCL, Open3D, CUDA). Familiarity with various depth sensing modalities (e.g., LiDAR, stereo vision, time‐of‐flight) and sensor fusion. Excellent problem‐solving skills, attention to detail ...

dependable working rhythm. Experience working with HPC environments or training large models in distributed settings, along with some exposure to GPU-level optimisation using CUDA or ROCm. A track record of taking models end-to-end, particularly in the context of LLMs, is valuable. Prior academic publications or meaningful ...

Familiarity with development to design and coding standards . Good teamwork and communication abilities Useful to have Python, C#, LabVIEW software development Experience CUDA integration (AI/ML) Industrial protocols (read-across to Synergia etc.) Experience with Infrared Physics, Computer Science, Software Engineering, ITQSB or Relevant Higher Level Formal ...

Tune, or similar frameworks Strong engineering practices: reproducible experimentation, clean code, testing, and performance-aware debugging Comfort with modern ML infrastructure (e.g., Docker, CUDA, Kubernetes, experiment tracking tools such as Weights & Biases) PREFERRED Experience with multimodal or multi-task model architectures Training and inference optimization (e.g., mixed precision, kernel ...

data structures and algorithms, and a rigorous coding style. Proficient in GPU/NPU architecture, with hands-on development and optimisation experience in CUDA C/C++ or Triton. Strong self-drive and technical curiosity; ability to proactively track the most cutting-edge AI Infra trends in the industry. ...

lifecycle of models in a multi‐tenant, high‐availability system. Familiarity with building ML inference services, model serialization (e.g., ONNX), and GPU programming (CUDA). You've built or worked on custom storage or job‐queueing systems before and have the scars to prove it. Maybe ...

models. Scale & Performance: Experience training models across distributed systems (multi-GPU/multi-node) and optimising training and inference performance (e.g., XLA, Triton, CUDA, Pallas).Domain Knowledge: A strong interest in, or knowledge of, biochemistry, computational biology, or drug discovery fundamentals. Industry Experience: Proven track record working in reputable ...

managing capacity planning, autoscaling policies, and driving post-incident remediation. Preferred Experience with low-level systems and high-performance computing components (e.g., C++ development, CUDA kernels, NCCL/SHARP, RDMA/NUMA, or GPU interconnect topologies). Active open-source or production contributions to modern inference frameworks (e.g., vLLM ...

architectures, including cross-architecture deployability and strict QoS timing constraints. Familiarity with High-Performance Computing (HPC), NVIDIA GPU acceleration, and hardware optimization (e.g., CUDA, TensorRT). Native fluency in Linux, Docker, and CI/CD pipelines—you know how to deploy and scale headless simulations unattended. Experience building ...

Strong Preference For Experience integrating accelerator hardware (GPUs, FPGAs, ASICs, NPUs, or novel architectures) into customer inference workflows Familiarity with the NVIDIA inference stack - CUDA, TensorRT, Triton Exposure to disaggregated inference architectures, prefill/decode separation, or KV cache management Compensation & Benefits Highly Competitive Salary: We are not saying ...