Senior AI Storage Infrastructure Engineer

Location: San Jose, CA / Remote
Company: Bitdeer Technologies Group
Employment type: Full-time
Salary: 180000–260000 USD per year

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.

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.