Distributed Storage Engineer for AI GPU Cloud

Bitdeer (NASDAQ: BTDR); Penang; On-site; MYR 120,000 - 240,000; Full time

Job summary

Bitdeer is hiring a storage/infra engineer to design and operate distributed file systems for its GPU cloud environment. You will own end-to-end storage integration, tuning for AI workloads, and multi-region data distribution, with a focus on reliability and scalability.

You will work with Compute, Network, and Control Plane teams to deploy, monitor, and maintain cutting-edge storage systems across regions, leveraging Ceph/Lustre/GPFS and related technologies.

Qualifications

• 3+ years (Senior 6+) in storage engineering or platform infrastructure.
• Strong understanding of distributed file system internals — data/metadata separation, replication, consistency models, POSIX vs object semantics.
• Proven experience operating distributed storage in production (Ceph, Lustre, GPFS/Spectrum Scale, BeeGFS, JuiceFS, MinIO).

Responsibilities

• Design and integrate distributed / parallel file systems into the GPU cloud, optimized for AI training / inference I/O patterns.
• Own end-to-end distributed-storage integration: provisioning, mounting, multi-tenant isolation, quota, and lifecycle within the platform / control plane.
• Tune storage throughput and latency for large-scale parallel access; benchmark across GPU SKUs and workloads.
• Architect multi-region storage: data locality, replication / consistency, durability, and cross-region access.
• Own golden images, templates, GPU drivers / CUDA, and the container / image registry, including versioned release and multi-region distribution.
• Build monitoring, capacity planning, and runbooks; eliminate single points of failure.
• Partner with Compute (delivery), Network (storage fabric / RDMA), and Control Plane (provisioning / quota) teams.

Skills

Distributed file systems

Linux

Python

Go

CI/CD

RDMA

Tools

Ceph

Lustre

GPFS/Spectrum Scale

BeeGFS

JuiceFS

MinIO

Job description

Bitdeer is hiring a storage/infra engineer to design and operate distributed file systems for its GPU cloud environment. You will own end-to-end storage integration, tuning for AI workloads, and multi-region data distribution, with a focus on reliability and scalability.

You will work with Compute, Network, and Control Plane teams to deploy, monitor, and maintain cutting-edge storage systems across regions, leveraging Ceph/Lustre/GPFS and related technologies.