高级 AI 存储基础设施工程师
Senior AI Storage Infrastructure Engineer
Bitdeer 是一家全球领先的 AI 和比特币挖矿基础设施技术公司
Bitdeer 致力于为客户提供全面的比特币挖矿解决方案，并构建 AI 计算基础设施以支持 AI 革命。Bitdeer 负责计算过程中涉及的复杂流程，包括设备采购、运输物流、数据中心设计与建设、设备管理以及日常运营。Bitdeer 还为对人工智能有高需求的客户提供先进的云服务能力。
总部位于新加坡，Bitdeer 在包括美国、挪威、不丹和埃塞俄比亚在内的多个国家部署了数据中心。
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职位概述
我们正在寻找一名高级 AI 存储基础设施工程师，负责构建我们 AI 原生 NeoCloud 的关键数据传输架构。AI 模型训练和推理对 I/O 有极高的需求；您将负责设计高性能存储解决方案，消除瓶颈并确保 GPU 持续获得数据。该职位位于分布式存储、内核级 I/O 和 Kubernetes 编排的交汇点。您将设计路径——从用于大规模 LLM 权重的 NVMe 支持本地缓存到并行文件系统集成——以实现大规模分布式训练和推理工作负载的无缝、低延迟访问。
主要职责
- 设计、部署和维护针对高性能并行文件系统（例如 Weka、Lustre、DAOS、VAST）的可靠容器存储接口（CSI）驱动程序。
- 架构和实现 GPUDirect Storage（GDS）集成，以在 NVMe 驱动器和 GPU 内存之间实现直接内存访问（DMA），绕过 CPU 瓶颈。
- 开发和管理本地 NVMe 缓存策略，以在分布式训练期间快速、低延迟地加载大规模模型权重和数据集。
- 优化整个容器化存储堆栈的 IOPS、吞吐量和延迟配置文件，从存储阵列到容器运行时。
- 与 GPU 系统与网络团队合作，确保存储层完全针对 RDMA 和高速互连（InfiniBand、RoCE）进行优化。
- 实现存储性能的自动化监控和警报，提前检测和缓解 I/O 竞争或硬件退化，避免影响生产任务。
- 在 Kubernetes 中定义存储策略、配额管理和多租户隔离策略，以确保 f
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Bitdeer is a world-leading technology company for AI and Bitcoin mining infrastructure.
Bitdeer is committed to providing comprehensive Bitcoin mining solutions for its customers and building AI computational infrastructure to support the AI revolution. Bitdeer handles complex processes involved in computing such as equipment procurement, transport logistics, data center design and construction, equipment management, and daily operations. Bitdeer also offers advanced cloud capabilities to customers with high demand for artificial intelligence.
Headquartered in Singapore, Bitdeer has deployed data centers across multiple countries, including the United States, Norway, Bhutan, and Ethiopia.
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Position Overview
We are seeking a Senior AI Storage Infrastructure Engineer to build the critical data-delivery fabric of our AI-native NeoCloud. AI model training and inference are profoundly I/O intensive; you will be responsible for architecting high-performance storage solutions that eliminate bottlenecks and ensure GPUs are constantly saturated with data. This role sits at the intersection of distributed storage, kernel-level I/O, and Kubernetes orchestration. You will design the pathways—from NVMe-backed local caching for massive LLM weights to parallel file system integration—that enable seamless, low-latency access for large-scale distributed training and inference workloads.
Key Responsibilities
- Design, deploy, and maintain robust Container Storage Interface (CSI) drivers for high-performance parallel file systems (e.g., Weka, Lustre, DAOS, VAST).
- Architect and implement GPUDirect Storage (GDS) integrations to enable direct memory access (DMA) between NVMe drives and GPU memory, bypassing CPU bottlenecks.
- Develop and manage local NVMe caching strategies for rapid, low-latency loading of massive model weights and datasets during distributed training.
- Optimize IOPS, throughput, and latency profiles across the entire containerized storage stack, from the storage array to the container runtime.
- Collaborate with the GPU Systems & Fabric team to ensure the storage layer is fully optimized for RDMA and high-speed interconnects (InfiniBand, RoCE).
- Implement automated monitoring and alerting for storage performance, detecting and mitigating I/O contention or hardware degradation before it impacts production jobs.
- Define storage policies, quota management, and multi-tenancy isolation strategies within Kubernetes to ensure fair resource sharing for customer workloads.
- Mentor junior engineers and drive architectural design reviews to maintain high standards of reliability and performance across the infrastructure team.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or a related field.
- 5+ years of experience in distributed storage systems and high-performance file systems, with a deep understanding of POSIX compliance and file I/O semantics.
- Deep expertise in the Kubernetes CSI paradigm, including building or extending volume plugins and storage operators.
- Strong hands-on experience with block/file I/O at the Linux OS level and kernel-level performance tuning.
- Familiarity with high-throughput networking protocols (RDMA, InfiniBand, RoCE) and how they interact with storage subsystems.
- Proven track record of operating, debugging, and scaling large-scale storage environments in production or HPC settings.
- Experience with infrastructure automation tools (e.g., Terraform, Ansible) and CI/CD pipelines.
- Excellent technical communication skills, with the ability to influence cross-functional architectural decisions.
- Experience working in high-velocity, high-growth engineering environments is strongly preferred.
Bitdeer is committed to providing equal employment opportunities in accordance with country, state, and local laws. Bitdeer does not discriminate against employees or applicants based on conditions such as race, color, gender identity and/or expression, sexual orientation, marital and/or parental status, religion, political opinion, nationality, ethnic background or social origin, social status, disability, age, indigenous status, and union.
Salary: $180,000 – $260,000Originally posted on Himalayas