1 to 25 of 86 CUDA Jobs in England

Resource Definitions (CRDs), Operators, and the Kubernetes API server architecture. GPU Ecosystem Experience: Hands‐on experience managing NVIDIA GPU clusters. Familiarity with NVIDIA drivers, CUDA toolkit, and the container runtime (NVIDIA Container Toolkit). Linux Internals: Deep understanding of the Linux kernel, cgroups, namespaces, and system performance tuning. Infrastructure ...

MLOps technologies such as Docker/containers, Kubernetes, datacenter compute/network/storage deployments. Hands‐on experience with NVIDIA systems/SDKs (e.g., CUDA), NVIDIA Networking technologies (e.g., DPU, RoCE, InfiniBand), ARM CPU solutions, coupled with proficiency in C/C++ programming, parallel programming, and GPU development. ...

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 ...

building things properly the first time, even under early‐stage constraints. Nice to Have Experience with GPU cluster management and ML training workloads (NVIDIA, CUDA, distributed training). Familiarity with MLOps tooling: Experiment tracking (MLflow, Weights & Biases). Workflow orchestration (Airflow, Prefect, Argo). Data versioning (DVC). Background ...

with Infrastructure as Code (Terraform). Experience with Azure. Identifying and resolving bottlenecks in distributed machine learning workloads (knowledge of low-level languages and CUDA library is a plus). Experience in building design automation products within the construction industry. Additional InformationOur Hiring Process 25-minute screening call Take ...

operators and custom controllers for ML workloads Advanced Slurm administration including multi-cluster federation and advanced scheduling policies Familiarity with GPU cluster management and CUDA optimization Experience with other ML frameworks like TensorFlow or distributed training libraries Background in HPC environments, parallel computing, and high-performance networking Knowledge ...

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 ...

ingestion, transformation, validation, versioning, republish — ideally including real‐world sources with rate limits, auth, or undocumented APIs. Hands‐on GPU compute and performance debugging (CUDA/NCCL, GPU utilization, networking bottlenecks, profiling). Working knowledge of cloud environments (AWS, GCP, or Azure), including object storage, IAM, and cost awareness. ...

well within a multinational team and with multinational customers. Excellent cultural awareness is essential. Desirable Experience developing firmware or drivers for GPUs. Knowledge of CUDA or OpenCL Experience working on upstreaming of kernel code/contributing to Linux kernel. Exposure to integration with data centre/cloud service operations ...

well within a multinational team and with multinational customers. Excellent cultural awareness is essential. Desirable Experience developing firmware or drivers for GPUs. Knowledge of CUDA or OpenCL Experience working on upstreaming of kernel code/contributing to Linux kernel. Exposure to integration with data centre/cloud service operations ...

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 ...

managing the 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 you don’t tick ...

compute workloads Infrastructure-as-code mindset: Terraform, Ansible, or equivalent; CI/CD pipelines (GitHub Actions, Jenkins, or similar) Experience with hardware-accelerated compute (CUDA/GPU workflows, profiling, performance tuning) — even if not on custom silicon Strong debugging and observability skills: distributed tracing, logging, metrics dashboards Ability ...

environments. Bonus Experience Any of the following would be advantageous: Quantitative finance Machine learning Scientific computing High-performance computing (HPC) Distributed systems GPU programming (CUDA) Optimisation Statistics Numerical methods Time-series modelling Cloud infrastructure Docker or Kubernetes Why This Role? This is an opportunity to work on problems that ...

research goals with practical engineering constraints Strong problem-solving skills, results orientation, and excellent collaborative communication Reliable and predictable availability Bonus Points Expertise in: CUDA kernel development, Triton/Pallas/CuTe DSLs, PyTorch/JAX internals, XLA optimization, or hardware acceleration (FPGA/ASIC) Knowledge of reinforcement learning ...

training frameworks, GPU/accelerator optimization, data pipelines, experiment tracking, and making research reproducible and reliable. You have experience down to the level of CUDA kernels or XLA. You have contributed to open source projects like PyTorch, JAX, MLX or similar. You care about research outcomes as much ...

modern inference frameworks such as vLLM, Triton, TensorRT-LLM, Ray Serve, or TorchServe. Deep experience with GPU systems engineering and hardware performance optimization (CUDA, NCCL, RDMA, NUMA, or GPU interconnects). Direct exposure to large-scale AI/ML infrastructure or hyperscale cloud environments. Wondering ...

independently drive research projects from idea generation through experimentation and publication. Nice to Have Experience with classical reconstruction tooling (COLMAP etc). C++ or CUDA, and a feel for performance and on-device constraints. #J-18808-Ljbffr ...

TensorFlow; comfortable with distributed training (DeepSpeed/FSDP/SLURM/K8s). Experience in deep learning, NLP or LLMs; bonus for CUDA or data‐pipeline chops. Strong software‐design instincts: testing, code review, CI/CD. Self‐starter, low‐ego, collaborative. Benefits France 💰 Competitive cash salary and equity ...

independently drive research projects from idea generation through experimentation and publication. Nice to Have Experience with classical reconstruction tooling (COLMAP etc). C++ or CUDA, and a feel for performance and on-device constraints. Compensation & Benefits £96,300 - £107,000 + bonus/equity/benefits Location & Work Model ...

Azure) and container orchestration (Kubernetes, Docker) as well as dealing with custom on-prem/neocloud offerings Proficiency in writing and optimising custom CUDA kernels for deep learning training is a nice-to-have but not imperative WHAT’S IN IT FOR YOU? Full relocation to Australia Competitive salary ...

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 tech companies ...

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 ...

operating large-scale AI, machine learning, or distributed computing platforms in enterprise environments.Deep understanding of LLM architectures and their interaction with GPU infrastructure, including CUDA, cuDNN, NCCL, kernel-level acceleration libraries, and distributed training frameworks such as PyTorch.Strong knowledge of distributed training and inference strategies, including tensor, pipeline, data ...

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 ...