GPU Systems 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 GPU Systems Engineer optimises GPU performance, utilisation and scheduling across high-density, multi-tenant environments.

The role works from configuration and tuning to profiling tools and production clusters, and documents what works for internal teams and customers.

Reporting Line

Reports to: Co-CTO and CPO

Key Responsibilities

• System configuration: design and optimise GPU system configurations for AI training and inference.
• Bottleneck analysis: analyse performance bottlenecks across compute, memory, networking and storage.
• Scheduling and utilisation: work on GPU scheduling, placement and utilisation strategies.
• Multi-tenant tuning: tune systems for high-density and multi-tenant GPU environments.
• Tooling: build tools for profiling, benchmarking and monitoring GPU workloads.
• Production support: support deployment and optimisation of GPU clusters in production.
• Collaboration: work with research, platform and infrastructure teams on GPU findings.
• Documentation: document system behaviour and best practices for internal and customer use.

Required Qualifications

• 5+ years of experience in systems engineering or performance optimisation
• A strong understanding of GPU architectures and accelerators
• Experience with Linux systems and low-level performance tuning
• Comfort working close to hardware and infrastructure layers
• Strong problem-solving and analytical skills

Bonus Qualifications

• Experience with CUDA, ROCm or GPU kernel optimisation
• Familiarity with NCCL, RDMA or high-speed interconnects
• Experience with AI workloads and frameworks
• Background in high-performance computing environments

What Success Looks Like (First 90 to 180 Days)

• Baseline profiling and benchmarks exist for the main GPU configurations
• Measured utilisation or performance gains on at least one production cluster
• Profiling and monitoring tools are in use by other teams
• Best-practice documentation is published for internal and customer use

Why This Role Matters

• Capacity customers pay for: each point of GPU utilisation recovered is sellable compute.
• Three wins at once: the work improves performance, efficiency and cost together.

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 185,000 to 235,000 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?

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