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Senior engineer builds and runs Kubernetes-native benchmarking services to measure latency, throughput, and reliability across CoreWeave’s global AI cloud, publishing results like MLPerf.
Senior engineer designing and scaling high-performance distributed storage systems for AI workloads using RDMA, GPU Direct Storage, and S3-compatible object storage.
Staff Engineer to design, build, and operate the control plane for CoreWeave's high-performance AI storage platform, working with exabyte-scale S3-compatible object storage, distributed filesystems, and technologies like RDMA, GPU Direct Storage, and InfiniBand to power AI workloads at scale.
Leads architecture and performance for CoreWeave’s Kubernetes-native AI inference platform, optimizing low-latency, high-throughput systems and GPU resource management.
Leads product strategy for CoreWeave’s AI training infrastructure, focusing on high-performance orchestration tools like Slurm on Kubernetes and reinforcement learning pipelines to support frontier AI research.
Build and optimize AI infrastructure performance monitoring tools, ensuring high availability and observability for CoreWeave’s cloud platform.
阿里云正在构建面向 AI 大模型训练与推理的下一代高性能网络基础设施，以支撑全球企业对极致算力与低延迟网络的需求。我们正在寻找一位在 RDMA（Remote Direct Memory Access） 技术领域有深厚经验的 DevOps 工程师，负责设计、部署和优化基于 RDMA 的 AI 训练集群网络架构，推动高性能网络技术在分布式 AI 场景的落地与创新。 核心职责： 1）AI 训练集群网络架构设计与运维 部署、运维和维护基于…
a. 阿里云正在构建面向 AI 大模型训练与推理的下一代高性能网络基础设施，以支撑全球企业对极致算力与低延迟网络的需求。我们正在寻找一位在 RDMA（Remote Direct Memory Access） 技术领域有深厚经验的 DevOps 工程师，负责设计、部署和优化基于 RDMA 的 AI 训练集群网络架构，推动高性能网络技术在分布式 AI 场景的落地与创新。 b. 核心职责： i. AI 训练集群网络架构设计与运维 1.…
Build and run the distributed GPU infrastructure that trains and serves PhysicsX’s large AI models for engineering simulation, partnering with research scientists to optimize training pipelines and model deployment.
About Invisix Semiconductor manufacturers can't build what they can't measure. Invisix Measuring Systems is a fast-growing scale-up in the high-tech industry, developing next-generation metrology solutions for…
团队介绍：AI芯片团队深耕人工智能芯片研发领域，产品覆盖云端与终端场景，致力于提供高效率、低成本的AI芯片及 SoC 解决方案，满足公司内外AI及相关业务的算力需求。团队自主设计了多款AI芯片，涵盖推理、训练场景，具有高性能、高精度、高灵活性、编译器友好、低成本等特点。核心AI加速核与AI软件栈均为自主研发，具备完整知识产权，能够支撑面向不同模型、不同部署场景的持续演进与优化。…
1、负责 GPU/异构计算集群的交付、上线、变更和日常运维，保障AI Infra稳定运行。 2、参与 AI 基础设施运维平台建设，推动监控告警、故障诊断、故障自愈和变更管理等能力落地。 3、建立并持续优化 SLI、SLO、SLA 和应急响应机制，参与重大故障处置、复盘及改进闭环。 4、制定集群交付标准和运维规范，通过自动化工具与平台减少重复操作，提升交付效率和资源可用率。…
我们是谁？ 作为中国领先的数字地图内容及导航服务提供商，高德地图日均服务数亿用户出行决策，每日处理超百亿级位置数据。视觉技术中心是驱动高德实现厘米级高精地图、实时三维重建、多模态感知等核心技术的引擎，持续突破自动驾驶、AR导航、智慧交通等领域的技术边界。 团队gihub主页：https://github.com/amap-cvlab 为何加入我们？ 挑战世界级技术难题，追求智能上限 用AI驱动国民级APP的产品迭代和颠覆式创新…
Optimize distributed AI training for 100B+ parameter models across 100k+ GPUs, writing CUDA/Triton kernels and co-designing hardware-efficient training recipes.
About Us We are an early-stage technology company operating at the intersection of Energy and AI infrastructure , focused on developing a new generation of efficient and scalable computing infrastructure. Our approach…
A staff-level network engineer who architects, owns, and operates Crusoe's dedicated RDMA-based storage network end to end (spine to leaf), debugs fabric issues down to TCP/IP, and builds automation and tooling for it. Core technologies: L2/L3 networking, RDMA/RoCEv2, switches, NIC drivers, and SDN.
Owns serving Luma's multimodal AI models at scale: integrating new architectures into the inference engine, managing GPU fleets across thousands of machines, and building scheduling, deployment, and reliability tooling. Core stack: Python, PyTorch-based serving (vLLM/SGLang/TensorRT-LLM), Linux, Docker, Kubernetes, Redis, S3.
Overview Working at Atlassian India Atlassian's mission "to unleash the potential of every team" is the guiding light behind what we do. Our products, including Jira, Confluence and Bitbucket, help teams…
Designs and builds the networking backbone for a next-gen AI cloud platform, focusing on multi-tenant isolation, high-performance connectivity, and automation across vendors like Arista and Juniper.
Staff-level data center network engineer who designs, automates, and operates the network powering a multi-tenant AI cloud platform — GPU clusters, tenant isolation, and high-performance connectivity. Hybrid (~3 days/week) in Bellevue, WA, working across Arista, Juniper, HPE, and similar platforms with network automation/IaC.
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