Jobs at Bitdeer Group

Develop large-scale AI cloud and infrastructure platforms across cloud services, Kubernetes, distributed training, model serving, AI agents, MLOps, security, and high-performance systems. New graduates may work in cloud platform engineering, SRE, AI operations, model serving, AI application engineering, security engineering, or AI research, with opportunities to rotate across areas.

Leads product strategy, portfolio, commercialization, and organizational development for a global AI cloud infrastructure platform. Oversees GPU compute, bare metal, containers, networking, storage, AI training and inference capabilities. Owns roadmap, pricing, packaging, metering, SLAs, revenue, margins, utilization, and customer adoption. Partners across engineering, infrastructure, operations, sales, and solutions while building a high-performing product organization.

Build, operate, and optimize Linux compute and storage infrastructure for an engineering lab. Responsibilities include system administration, Shell automation, provisioning, configuration management, storage management, monitoring, resource scheduling, troubleshooting, networking, security controls, performance optimization, and documentation. The role supports high availability and reliable performance across Linux workloads.

Manage and strategize patents, collaborate with R&D on innovations, handle IP disputes, and drive compliance and training efforts.

Design, deploy, and operate production Kubernetes control planes for large GPU clusters. Implement GPU-specific scheduling, CRDs, multi-tenant isolation, BMaaS provisioning, Terraform-based IaC, monitoring, SLI/SLOs, and automated remediation workflows to enable autonomous AIOps-driven recovery and tenant self-service.

Build and maintain SRE microservices supporting a global GPU infrastructure platform. Implement GitOps, declarative configuration, and CI/CD automation; monitor metrics, logs, and traces; support operational readiness through on-call participation, runbooks, and incident reviews; and write unit, integration, and end-to-end tests. Work with senior engineers to deliver production-ready platform features while maintaining service reliability and infrastructure consistency.

Designs and operates multi-tenant SDN, VPC overlays, and high-performance InfiniBand or RoCE GPU network fabrics. Responsibilities include tenant isolation, cross-region routing, congestion control, network automation, change management, fault localization, architecture standards, security boundaries, and observability for a large-scale multi-region GPU cloud.

Architect and scale high-cardinality observability and telemetry infrastructure for AI cloud platforms. Build Kubernetes exporters, eBPF diagnostics, hardware and network monitoring, dashboards, alerting, and GPU/network utilization billing pipelines. Establish telemetry standards for AI workloads, support proactive remediation, collaborate with GPU and scheduling teams, lead architecture reviews, and mentor engineers. The role requires expertise in Go, Kubernetes, Prometheus/OpenTelemetry, Linux performance tuning, eBPF, AI hardware metrics, and large-scale cloud or HPC telemetry systems.

Architect and build a highly available Kubernetes control plane for an AI-native cloud platform managing thousands of clusters. Develop Operators and CRDs, optimize etcd, implement multi-tenant security, enable zero-downtime upgrades and federation, and integrate with GPU orchestration systems. Lead distributed systems architecture, reliability engineering, design reviews, and team mentorship while preventing control-plane failures from affecting customer workloads.

Own end-to-end reliability for a customer-facing GPU cloud serving workloads across 100–10,000 GPUs. Responsibilities include operating Kubernetes GPU clusters, managing multi-tenant isolation, automating bare-metal provisioning, defining SLIs/SLOs/SLAs, handling incidents and customer communications, building observability with Prometheus and Grafana, implementing infrastructure as code and GitOps, and developing automated remediation and self-service operational tooling.

Lead identification, application, and management of US federal, state, and local tax abatement, exemption, and incentive programs (85%). Support US tax advisory, compliance, and liaison with tax authorities (15%), model financial impact, maintain centralized tracking, manage renewals, and coordinate with stakeholders and external advisors to ensure compliance and optimize tax savings.

Owns architecture, deployment, operations, reliability, and multi-tenant scheduling policy for production Slurm HPC GPU clusters across bare-metal and virtualized environments. Leads Slinky Kubernetes integration, elastic capacity sharing, containerized workloads, GPU and fabric health monitoring, infrastructure automation, observability, accounting, and billing integration. Provides customer onboarding and escalation support, writes operational documentation, leads incident response, and mentors platform engineers.

Deploy, operate, and optimize high-performance parallel/distributed storage systems for AI training and inference; implement multi-tenant isolation and GPU Direct data paths; instrument telemetry for storage-fault prediction; convert incidents into automated runbook-as-code; plan capacity, firmware, migrations, and DR for GPU clusters.

Front-line SRE covering 8AM–8PM PST monitoring GPU clusters, networking, storage, and sensors. Execute runbooks, perform hardware triage and physical DC tasks (rack, cable, swap), collect diagnostics for escalation, manage tickets (ServiceNow/Jira), update runbooks, and tag incidents to train the AIOps platform.

Build global, multi-region cloud control-plane services for GPU infrastructure, including tenant management, quotas, metering, billing, ticketing, RBAC, customer portals, and APIs. Design cross-region consistency, automate customer onboarding, ensure billing auditability, develop operational recovery tooling, and support region-aware scheduling and placement.

Design, implement, and maintain CI/CD and MLOps pipelines, provision and scale cloud-native AI infrastructure (Kubernetes, GPU clusters), enforce IaC practices, implement observability and security frameworks, lead incident response and cross-functional platform engineering to ensure high availability and compliance.

Lead end-to-end architecture of AI data center networks, DCI, and global backbone for large GPU clusters. Design underlay and overlay networks, IP/VLAN/VXLAN planning, congestion control (PFC/ECN/INT), and optical transport. Produce HLD/LLD, topology diagrams, standards, and SOPs; engage vendors and drive risk mitigation and network improvements.

Lead the architecture and implementation of AI workload scheduling and orchestration for a large-scale NeoCloud platform. Build batch and gang scheduling, admission control, queueing, topology-aware placement, GPU sharing, and multi-tenancy capabilities using Kubernetes technologies. Optimize NVLink and InfiniBand utilization, integrate scheduling with hardware and storage systems, improve reliability in HPC environments, resolve contention and deadlocks, and mentor engineers while guiding architectural decisions.

Architects and operates high-performance storage infrastructure for AI training and inference. Responsibilities include developing Kubernetes CSI drivers, integrating GPUDirect Storage, optimizing NVMe caching and I/O performance, supporting RDMA and high-speed networking, monitoring storage systems, managing quotas and multi-tenancy, and mentoring engineers.

Architects and operates the hardware foundation for AI cloud infrastructure, integrating NVIDIA and AMD GPUs with Kubernetes. Configures high-performance networking using RDMA, SR-IOV, RoCEv2, and InfiniBand; automates GPU and NIC remediation; manages GPU slicing, kernel and driver tuning, bare-metal provisioning, and firmware updates. Investigates cross-layer performance issues, supports topology-aware scheduling and storage, establishes operational standards, and mentors engineers.