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Staff Software Engineer at Graphcore designs and builds a high-performance communication library for AI hardware clusters, using C++, Python, and modern parallel computing techniques.
Designs and scales ultra-high-bandwidth, low-latency data center networks for AI workloads, focusing on RDMA, Clos fabrics, and automation.
Location: Ra'anana #Hybrid DriveNets is a leader in high-scale disaggregated networking solutions. Founded in 2015, DriveNets modernizes the way service providers, cloud providers and hyperscalers build networks.…
Build and lead the end-to-end QA team for DriveNets' AI Cluster Orchestrator, owning quality from test planning to release sign-off across UI, REST APIs, databases and real AI infrastructure (GPU clusters, leaf-spine fabrics, BGP/EVPN). Requires CCNP-level networking and strong Linux skills; QA automation, Kubernetes and AI-agent workflows are pluses.
End-to-end QA engineer for DriveNets' AI Cluster Orchestrator (hybrid, Ra'anana), designing and executing tests across UI, REST APIs, databases and real AI cluster infrastructure — GPU servers, leaf-spine fabrics, BGP/EVPN, telemetry and performance benchmarks — using Linux, networking tools and AI agents.
Job Summary : We are looking for a skilled Software Engineer III to design, develop, and optimize userspace block devices on Linux. The role involves working close to the Linux kernel, io_uring, and storage subsystems…
Design and operate the datacenter networks connecting large GPU clusters for an AI infrastructure startup — training fabrics (InfiniBand/RoCE), storage, and management networks. Day to day: deploying scalable topologies, automating provisioning, benchmarking and troubleshooting performance, and building monitoring so distributed ML workloads never bottleneck on the network.
Builds and operates the storage infrastructure feeding Prime Intellect's frontier AI platform — designing, deploying, and tuning parallel filesystems, object storage, and NVMe caching for training datasets, checkpoints, and model artifacts on scaling GPU clusters, with a focus on throughput, durability, and reliability.
Senior Software Engineer builds and optimizes a Kubernetes-native cloud platform for AI inference workloads, focusing on low-latency, high-scale GPU services and performance tuning.
About the Team The Seed Infrastructures team oversees the distributed training, reinforcement learning framework, high-performance inference, and heterogeneous hardware compilation technologies for AI foundation…
Network performance engineer optimizing NIC, PCIe, driver, kernel, and RDMA communication for single- and multi-host AI inference clusters — improving KV cache transfer, cross-node collective communication, and model-weight loading, while troubleshooting packet loss and latency. Core stack: Linux, RDMA (RoCEv2/InfiniBand), C/C++, plus Python/Go/Shell.
VAST Data is hiring a Forward Deployed Engineer to embed with strategic enterprise customers — troubleshooting storage, networking, and infrastructure issues at scale, writing scripts/automation to unblock workflows, and feeding field learnings back into the product roadmap. Core tech: distributed storage systems, Linux, InfiniBand/RoCE networking, Python, C/C++, Docker/Kubernetes.
Designs and deploys end-to-end AI infrastructure for enterprises, integrating NVIDIA GPU clusters, high-performance networking, parallel storage, and AI software stacks to support large-scale model training and inference.
团队介绍：字节跳动基础设施网络团队，负责集团全球网络基础设施设计、研发及运营工作，包括全球骨干网、数据中心网络、AI高速网络、云网络、容器网络、四七层应用网络等，基于先进的交换芯片和光互连技术，我们通过自研交换机、光芯片打造高性能的数据中心网络；基于自研RDMA协议、拥塞算法、集合通信库的创新，我们打造了超大规模的AI高速网络；基于全自研的SDN、NFV、端到端的QUIC协议、拥塞算法以及软硬结合HPA，我们设计了拥抱时代的云计算网络…
我们是专注于大模型服务平台的技术团队，我们的核心业务是提供高效稳定的大模型推理服务API，帮助各行各业轻松利用大模型的强大能力，正在寻找一位有实力的Site Reliability Engineer (SRE) 加入我们，共同打造更可到的大模型服务平台。 负责AI基础设施算力资源的规划、运营管理与精细化数据体系建设。通过系统化、数据化方法提升GPU资源利用率、周转率与成本效益，保障AI训练/推理任务的高效稳定运行，支撑业务快速发展。…
Bright Vision Technologies is a forward-thinking software development company dedicated to building innovative solutions that help businesses automate and optimize their operations. We leverage cutting-edge…
Clockwork Systems, founded by Stanford researchers, builds software-driven fabrics that boost GPU cluster utilization. This on-site Palo Alto role designs and implements low-level systems software for fault-tolerant, high-performance distributed GPU training, working deep in the internals of PyTorch, NCCL, and CUDA with strong C/C++ and distributed-systems skills.
Senior engineer on Radiant's Platform team who architects, builds, and operates an AI-native cloud platform — virtualisation, bare-metal provisioning, Kubernetes, high-performance networking/storage, security hardening, and observability — so teams can run HPC and AI/ML workloads reliably at scale. Core stack: Kubernetes, Terraform, Ansible, Go, Python, Linux.
Senior architect at Credo's San Jose office designing system-level memory and interconnect architecture that links XPUs (NPU/GPU/custom silicon) across node, rack, and cluster scale so large LLM training and inference run efficiently. Requires computer-architecture depth; hands-on NPU silicon production experience strongly preferred. Base pay $160k-$220k plus bonus and equity.
Owns the product experience and lifecycle for bringing new AI infrastructure clusters onto Radiant's platform — onboarding, validation, benchmarking, provisioning and automation — partnering with infrastructure, SRE, networking, storage and hardware teams. A highly technical role spanning GPUs, Kubernetes/Slurm, RDMA fabrics, distributed storage and observability.
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