About Zettabyte
AI computing company: we turn GPU hardware into reliable, high-utilisation AI compute through software, orchestration and full lifecycle operations.
- What we offer: enterprise GPU cloud and AI inference, software for data centre owners to run their own GPU clouds, turnkey and sovereign AI data centres, edge computing and GPU financing
- Who we work with: GPU owners, data centre operators, enterprises, OEMs, integrators and financiers across multiple markets
- How we work: fast-moving and high-accountability; we value speed, ambition, clarity, ownership and measurable outcomes
Role Summary
The Distributed Systems Engineer builds and operates the scalable, fault-tolerant distributed systems that power zCLOUD, including the control planes for GPU and resource orchestration. The role owns services from design through production operation.
Key Responsibilities
- zCLOUD systems: design and build the distributed systems that power the zCLOUD platform.
- Control planes: develop control planes for GPU and resource orchestration.
- Fault tolerance: build scalable, fault-tolerant services for multi-tenant environments.
- Performance at scale: improve system reliability, latency and throughput at scale.
- Integration: work with GPU systems and research teams on integration.
- Observability: implement observability, monitoring and debugging capabilities.
- Service ownership: own services from design through production operation.
- Documentation: document system architecture and operational best practices.
Required Qualifications
- 5+ years of experience building distributed systems
- A strong understanding of concurrency, consistency and fault tolerance
- Experience designing APIs and service-oriented architectures
- Comfort operating and debugging systems in production
- A strong ownership mindset and engineering rigour
Bonus Qualifications
- Experience with cloud platforms or large-scale infrastructure
- Familiarity with scheduling or resource management systems
- Experience supporting AI or data-intensive workloads
- Background in platform or control-plane engineering
What Success Looks Like (First 90 to 180 Days)
- At least one zCLOUD service owned end to end in production
- Control-plane improvements measured in reliability, latency or throughput
- Monitoring and debugging in place for owned services
- Architecture and operational practices documented
Why This Role Matters
- Built for GPUs: zCLOUD is a cloud designed for GPU-driven AI workloads.
- Always available: this role builds the control planes that keep it consistent and reliable.
Working Norms at Zettabyte
- Bias to action: deliver drafts quickly, then iterate on feedback
- Be precise: double-check numbers and facts; sloppy analysis creates downstream cost
- Make it usable: package outputs for decision-making, not data dumps
- Own the process: whoever drives a deliverable manages stakeholders and timelines
- Learn the ecosystem: understand GPUs, cluster operations and the buyer landscape
Interested in This Position?
Apply