26 to 33 of 33 Permanent CUDA Jobs

task breakdown: - Administer and maintain GPU-accelerated servers and clusters, including NVIDIA A100, H100, and other high-end GPU sets. - Manage and optimize NVIDIA software stack components such as CUDA, cuDNN, TensorRT, NCCL, and NGC containers. - Monitor system performance, troubleshoot hardware/software issues, and ensure high availability of AI infrastructure. - Collaborate with DevOps and AI teams to support … at least 3 years focused on NVIDIA GPU-based systems. - Deep understanding of Linux system administration, especially in HPC or AI environments. - Hands-on experience with NVIDIA GPU drivers, CUDA toolkit, and performance tuning. - Familiarity with Slurm, Kubernetes, or other job scheduling and orchestration tools. - Experience with monitoring tools (e.g., Prometheus, Grafana) and infrastructure automation (e.g., Ansible, Terraform). More ❯

Job Title: A.I. Engineer Location: Hybrid Work Model Reporting to Vienna, VA Pay Rate: Open to Both C2C and W2 options Position Type: Multiyear Contract Responsibility: Build and enhance machine learning models through all phases of development including design, training More ❯

Essential Skills Masters or higher degree in ML/AI, Computer Science/Engineering, or related disciplines Professional software development experience with modern C++ Experience with GPU compute in CUDA/OpenCL Excellent communication, teamwork and a results-oriented attitude Proficiency in problem-solving and debugging Expertise in image-based 3D reconstruction: Photogrammetry, Neural Radiance Fields (NERF) or Gaussian More ❯

CUDA Developer | High-Performance Computing | Applied AI Location: UK-based Remote Type: Contract, Outside IR35, Remote Sector: Advanced Computing/Applied AI We’re partnering with a company building next-generation GPU-accelerated software for scientific and AI applications. We are recruiting for a CUDA Developer who’s passionate about getting every ounce of performance out of modern … opportunity to work with a small, expert team where your technical decisions will shape the foundation of an emerging AI technology. What You’ll Be Doing Designing and optimising CUDA kernels for high-performance workloads. Translating advanced algorithms into production-ready GPU-accelerated code. Profiling performance and reducing bottlenecks using Nsight, CUPTI, and custom tooling. Working with C++ engineers … and ML researchers to deliver scalable AI computation pipelines. Contributing to architecture decisions on parallelisation, data transfer, and memory efficiency. What We’re Looking For Deep experience with CUDA C/C++ and modern C++ (17/20) . Strong understanding of GPU architecture, memory management, and parallelism . Familiarity with OpenMP, MPI, or other HPC frameworks . Bonus More ❯

to deploy GPU and ML workloads at scale. Provision and optimise GPU cloud infrastructure (AWS, GCP, Azure) using Terraform/Ansible. Collaborate with GPU engineers and researchers to integrate CUDA, SYCL, Vulkan, and ML kernels into production workflows. Support secure packaging, deployment, and distribution of GPU-accelerated software to partners and clients. Evolve infrastructure to support hybrid AI/… GitLab CI, etc.). Proficiency in containerisation and orchestration (Docker, Kubernetes). Experience with cloud GPU infrastructure (AWS, Azure, GCP) and IaC (Terraform, Ansible). Familiarity with GPU workflows (CUDA, SYCL, Vulkan, OpenCL) or HPC performance optimisation. Strong scripting and programming skills (Python, Bash, C/C++ exposure a plus). Knowledge of monitoring, logging, and performance testing for More ❯

System Maintenance and Performance Optimization • Manage, maintain, and tune bare-metal HPC clusters running Linux-based operating systems (e.g., Fedora, Debian, Ubuntu). • Optimize Nvidia GPU compute environments, including CUDA, NCCL, and GPU resource management in multi-node HPC clusters. • Oversee high-speed networking configurations, including InfiniBand (Mellanox), RDMA, and Ethernet fabric tuning for low-latency HPC workloads. • Configure … Support • Serve as the lead technical resource for diagnosing and resolving complex software, networking, and hardware issues in large-scale GPU clusters. • Analyze logs, conduct performance profiling, and debug CUDA, MPI, and RDMA-related issues. • Work closely with AI/ML research teams, cloud engineers, and enterprise clients to optimize workload performance. Collaboration and Process Improvement • Support the ongoing More ❯

architectures Develop low-latency inference systems providing real-time, high-accuracy predictions in production Optimise and extend machine learning frameworks to improve training and inference performance Leverage GPU programming (CUDA, cuDNN, TensorRT) to maximise efficiency Automate model experimentation, tuning and retraining in partnership with research teams Work with infrastructure specialists to optimise workflows and reduce compute costs Assess and … ML development and deployment Skills and Experience 5+ years’ experience in machine learning with a focus on training and inference systems Strong programming expertise in Python and C++ or CUDA Proficiency with PyTorch, TensorFlow or JAX Hands-on experience with GPU acceleration and distributed training (Horovod, NCCL or similar) Background in real-time, low-latency ML pipelines Familiarity with More ❯

architectures Develop low-latency inference systems providing real-time, high-accuracy predictions in production Optimise and extend machine learning frameworks to improve training and inference performance Leverage GPU programming (CUDA, cuDNN, TensorRT) to maximise efficiency Automate model experimentation, tuning and retraining in partnership with research teams Work with infrastructure specialists to optimise workflows and reduce compute costs Assess and … ML development and deployment Skills and Experience 5+ years’ experience in machine learning with a focus on training and inference systems Strong programming expertise in Python and C++ or CUDA Proficiency with PyTorch, TensorFlow or JAX Hands-on experience with GPU acceleration and distributed training (Horovod, NCCL or similar) Background in real-time, low-latency ML pipelines Familiarity with More ❯