1 to 25 of 60 Permanent CUDA Jobs

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 ...

fundamentals including TCP/IP, DNS, DHCP, VLANs, routing, and switching, with exposure to high‐performance networking environments Exposure to NVIDIA GPU ecosystems, including CUDA-based workloads and GPU‐accelerated compute environments, including the NVIDIA AI reference architecture. Familiarity with high‐performance networking technologies such as InfiniBand and RoCE ...

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 ...

such as MLFLOW a plus. • Experience with basic linux administration and software development in a linux environment. • Experience in hardware resource management and configuration - CUDA, Docker, KubeFlow, Kubernetes, etc a plus • Must possess qualities of being curious and eagerness to learn . • Experience with data visualization and ability ...

such as MLFLOW a plus.• Experience with basic linux administration and software development in a linux environment.• Experience in hardware resource management and configuration - CUDA, Docker, KubeFlow, Kubernetes, etc a plus• Must possess qualities of being curious and eagerness to learn .• Experience with data visualization and ability ...

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 ...

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. ...

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 ...

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 ...

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 ...

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 ...

including public code reviews, documentation, release notes, issue tracking, and community engagement. Vulkan or other low-level graphics and compute APIs, such as OpenCL, CUDA, DirectX, or Metal. Machine learning frameworks, inference runtimes, model formats, or model conversion tooling. CMake, cross-compilation, packaging, dependency management, or developer tooling. Performance ...

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 ...

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 ...

specific solutions (e.g., network stacks, bidding algorithms, data pipelines, etc.).* Familiarity with advanced computing, AI, and/or GPU acceleration platforms such as CUDA, Triton, NeMo* Track record in crafting and implementing systems for real-time processing and low-latency decision-making.* Experience working in customer-facing technical ...

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 ...

performance of XTX's training and inference platforms. The remit is wide, and you should expect to be challenged. We are not just writing CUDA code. Amongst other things, you will be working on a sophisticated optimising compiler for a variety of accelerated computing platforms. You should be proficient ...

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 ...

finance, trading, or quantitative research (not required). Publications, competition results (e.g., Kaggle, academic ML contests), or open-source contributions. Familiarity with C++, CUDA, or low-latency systems. Benefits Opportunity to work at one of the world's leading algorithmic trading firms Engaging projects offering accelerated responsibilities and ownership ...

PyTorch ecosystem Experience with inference optimisation techniques, including quantisation, speculative decoding and model parallelism across modern GPU architectures Strong software engineering skills, including Python, CUDA and building reliable systems for machine learning workloads Strong communication skills, with the ability to collaborate across research, infrastructure and engineering teams Why join ...