AI Infrastructure Engineer

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 AI Infrastructure Engineer designs and operates the infrastructure that supports large-scale AI training and inference workloads.

The role integrates compute, networking and storage for GPU-intensive systems and keeps production pipelines reliable, fast and cost-efficient.

Reporting Line

Reports to: Co-CTO and CPO

Key Responsibilities

• Infrastructure layers: build infrastructure layers that support large-scale AI workloads.
• Integration: integrate compute, networking and storage for GPU-intensive systems.
• Pipelines: support training and inference pipelines in production environments.
• Optimisation: improve performance, reliability and cost efficiency.
• System design: work with research and platform teams on system design.
• Automation: automate deployment and scaling of AI infrastructure.
• Troubleshooting: troubleshoot complex infrastructure and workload issues.
• Documentation: document deployment architectures and operational workflows.

Required Qualifications

• 4+ years of experience in infrastructure or systems engineering
• Hands-on experience running AI workloads in production
• A strong understanding of distributed infrastructure trade-offs
• Comfort working across software and hardware boundaries
• An execution focus with strong debugging skills

Bonus Qualifications

• Experience with containerisation and orchestration systems
• Familiarity with ML frameworks and AI pipelines
• Experience with high-throughput networking or storage systems
• Background in data centres or large-scale compute platforms

What Success Looks Like (First 90 to 180 Days)

• Training and inference pipelines run reliably with clear monitoring
• Deployment and scaling are automated for the main workloads
• Measured improvements in cost efficiency or reliability
• Deployment architectures are documented and reusable

Why This Role Matters

• Where it all meets: research, platform and real-world deployment come together in this role.
• Workloads at scale: customer AI training and inference run reliably and efficiently because of it.

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

Details

• Workplace: Hybrid, 3 days in office and 2 days remote, Palo Alto
• Employment type: Full-time
• Compensation: USD 175,000 to 225,000 a year base salary, plus equity
• Benefits: Medical, dental and vision insurance; 401(k) retirement plan; paid time off and US public holidays; equity
• Work authorisation: Required in the United States
• Equal opportunity: Zettabyte is an equal opportunity employer; qualified applicants with arrest or conviction records will be considered in line with the California Fair Chance Act

Interested in This Position?

Apply