GPU Systems Engineer

• 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.

Systems Engineering, GPU (Graphics Processing Unit), Artificial Intelligence (AI), High Tech Industry, GPU (Graphics Processing Unit), Computer Software, Network Operations Center, Cloud Computing, OEM (Original Equipment Manufacturer), Systems Administration/Management, * 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

• 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, * 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?