Principal AI Infrastructure Engineer - Highrise AI

About this role

About Highrise AI

Highrise AI is a GPU-native infrastructure platform built for modern AI workloads. Designed for developers and enterprises operating in performance-critical and security-sensitive environments, Highrise delivers scalable GPU compute across dedicated clusters, managed cloud, and confidential environments.

Highrise combines bare-metal performance with full-stack orchestration, API-driven access, and hardware-enforced isolation to support training, fine-tuning, and production-scale deployment of AI models. From reserved GPU capacity to enterprise-grade confidential compute, Highrise provides flexible infrastructure models tailored to the needs of builders operating at scale.

Beyond infrastructure, Highrise integrates disciplined operations, advanced automation, and on-site engineering expertise to sustain high uptime and predictable performance across deployments. These capabilities extend beyond Highrise-owned fleets: through managed services, Highrise also operates third-party GPU hardware on behalf of customers and partners, applying the same operational standards to infrastructure it does not own.

Highrise is a subsidiary of Hut 8 Corp., an energy infrastructure platform. Hut 8 integrates power, digital infrastructure, and compute at scale. Highrise serves as the compute and operations layer—designing, deploying, and operating next-generation GPU clusters from commissioning through production.

ROLE OVERVIEW

We are seeking a senior, hands-on AI Cluster Integration Engineer to bring large-scale GPU compute clusters from physical installation to validated, commercially ready production. Working alongside a team of senior infrastructure engineers, this person will own critical elements of cluster bring-up, bare-metal provisioning, advanced systems configuration, performance optimization, and acceptance testing.

After deployment, the engineer will provide Tier 3 technical escalation and sustained performance support for operating clusters. The focus is on compute-platform readiness and workload performance rather than primary ownership of network switch and fabric configuration.

KEY RESPONSIBILITIES

Cluster Bring-Up and Systems Integration

• Lead technical bring-up of materially complete, racked, powered, and cabled AI clusters through production readiness and customer handoff.

• Establish and validate system baselines across BIOS/BMC, firmware, GPU drivers, operating systems, and cluster software stacks.

• Deploy and optimize automated bare-metal provisioning, imaging, node configuration, and lifecycle workflows at scale.

• Configure and troubleshoot advanced Linux and GPU systems, including kernel, driver, accelerator, and host-level performance settings.

• Integrate and validate Kubernetes and Slurm environments in partnership with platform, networking, and operations engineers.

Performance Engineering and Acceptance

• Execute NVIDIA Field Diagnostics, L11/L12 validation, burn-in, stability, and hardware health qualification procedures.

• Run and analyze NCCL collective communication tests, GPU benchmarks, and distributed workload performance at node, rack, and cluster scale.

• Troubleshoot GPU, NVLink/NVSwitch, PCIe, RDMA, and InfiniBand/Ethernet performance issues, partnering with network engineers on fabric-related remediation.

• Identify configuration bottlenecks and optimize throughput, latency, scaling efficiency, and cluster reliability.

• Own technical acceptance criteria, test evidence, defect tracking, remediation verification, and readiness recommendations for commercial service.

Tier 3 Operations and Reliability

• Serve as a senior escalation point for complex production incidents involving compute nodes, GPU software, provisioning, scheduling, and degraded performance.

• Perform deep root cause analysis, coordinate vendor engineering escalations, and lead corrective actions for recurring or systemic issues.

• Support fleet-level upgrades, configuration changes, cluster expansions, and post-maintenance validation with appropriate change controls.

• Develop diagnostic tooling, automation, runbooks, and operational handoff material for SRE, NOC, and site support teams.

Cross-Functional Execution

• Work closely with infrastructure engineering, site operations, OEMs, and software/platform teams to resolve cross-stack issues and meet delivery milestones.

• Provide concise technical status, benchmark findings, risks, and acceptance recommendations to engineering leadership and stakeholders.

ABOUT YOU

Education and Experience

• Bachelor's degree in computer science, engineering, or a related field, or equivalent practical experience.

• 8+ years of hands-on Linux systems engineering, HPC, cloud, or large-scale infrastructure experience, including senior-level troubleshooting.

• Proven experience bringing GPU or HPC clusters into production and qualifying systems against performance and reliability targets.

• Strong expertise in Linux, automation (Python/Bash/Ansible), bare-metal provisioning, configuration management, and systems diagnostics.

• Hands-on experience with NVIDIA GPU platforms, NCCL, Field Diagnostics, and multi-node performance testing.

• Practical experience with Kubernetes and Slurm; familiarity with high-speed Ethernet, InfiniBand/RDMA, and parallel or distributed storage.

Core Competencies

• Deep technical curiosity and the ability to isolate difficult problems across hardware, firmware, operating systems, and distributed workloads.

• Strong ownership of outcomes, with a systematic approach to testing, documentation, root cause analysis, and continuous improvement.

• Comfortable collaborating with highly experienced peer engineers while driving independent technical decisions and execution.

• Clear communication with engineering leadership, vendors, site personnel, and customer-facing stakeholders.

ABOUT THE WORK ENVIRONMENT

This is a full-time, engineering role supporting large-scale AI compute…