BitDeer Technologies Group

Build autonomous, multi-agent AI systems that automate NeoCloud infrastructure and business workflows. This role combines agent architecture, RAG, LLM evaluation, and cloud-native platform integration to create reliable self-optimizing systems.

Own the quality gates that validate GPU infrastructure releases across drivers, images, regions, and complex compatibility matrices. You will build automated test suites and staged rollout processes to improve release velocity without increasing production incidents.

Integrate and operate distributed storage systems for Bitdeer’s multi-region GPU cloud at massive training and inference scale. The role also owns the node-delivery image, GPU driver, CUDA, and container registry pipeline.

Build the control-plane systems that turn a global GPU fleet into a scalable cloud product, including tenant management, billing, RBAC, customer portals, APIs, and self-service automation. This backend-focused role addresses multi-region consistency, operational stability, and region-aware scheduling.

Own the end-to-end reliability of a customer-facing GPU cloud serving mission-critical AI workloads across Kubernetes clusters ranging from 100 to 10,000 GPUs. You will build observability, automation, incident-response, multi-tenancy, and self-healing systems that make the service dependable for external tenants.

Join Bitdeer’s SRE/Monitoring Platform team as an early-career software engineer building the observability and automation foundation for a global GPU cloud. This mentorship-heavy temporary role offers hands-on experience across telemetry ingestion, storage, alerting, Kubernetes health, remediation, testing, and production operations.

Own the design, deployment, and operation of production Kubernetes control planes for large-scale GPU workloads, with topology-aware scheduling, multi-tenant isolation, and automated remediation. This role brings SRE practices and AIOps together to keep customer workloads reliable with minimal human intervention.

Build distributed systems and cloud infrastructure that power large-scale AI training and inference workloads. This fresh graduate role focuses on cluster management, scheduling, reliability, and performance at hyperscale.