5 days ago

Technical Lead - GPU Infrastructure

Jobgether

Australia

5 days ago

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Technical Lead - GPU Infrastructure needed for a fully remote role based in Australia, responsible for architecting and delivering a large-scale GPU infrastructure platform. Requires deep hands-on expertise in Slurm, Kubernetes, NVIDIA GPU operations on bare metal, and proven technical leadership of distributed engineering teams. The role combines systems architecture, team management, and partner-facing technical ownership.

Core AIRemoteFull-timeSeniorSlurmKubernetes

Technical Lead - GPU Infrastructure

Apply

Core AIRemoteFull-timeSeniorSlurm

Salary

Not specified

Work Location

Australia, AU

Work Model

Fully remote, working location between UTC and UTC+5:30, occasional travel to partner sites and team events

Experience Required

8 years

Employment Type

Full-time

Experience Level

8+ years hands-on engineering experience, including at least 3 years leading infrastructure platform teams

Core Qualifications

Technical (Must-have)

SlurmKubernetesNVIDIA GPUCUDAFabric ManagerNVSwitchDCGMMIGInfiniBandRDMASR-IOVNCCLLinuxJavaScriptNode.js

Soft Skills

Technical leadershipTeam managementCommunicationWritten communicationDecision-makingPeople leadershipCross-functional collaborationMentoringProblem solving

Preferred Qualifications

Technical (Nice-to-have)

SoperatorSlinkyKueueVolcanoKAIKubeflow TrainervLLMSGLangTensorRT-LLMKubeVirtKata ContainersQEMU/KVMFirecrackerIntel TDXAMD SEV-SNP

Key Responsibilities

•Own the end-to-end platform architecture, including architecture proposals, high-level and low-level designs, technical reviews, and ongoing architecture documentation.
•Lead and line-manage a distributed engineering team across backend, frontend, DevOps, QA, and documentation.
•Establish engineering standards, oversee code and design reviews, manage release gates, conduct one-to-ones, and provide growth and performance feedback.
•Design, build, and operate a managed Slurm service supporting research and model-training workloads.
•Own Slurm controllers, accounting, partitions, login nodes, node onboarding, acceptance testing, driver and CUDA baselines, upgrades, stalled-job detection, node health, draining, autohealing, storage visibility, identity, and workload isolation.
•Lead GPU infrastructure operations on bare-metal environments, including NVIDIA drivers, CUDA, Fabric Manager, NVSwitch, DCGM, MIG, node burn-in, and acceptance processes.
•Own Kubernetes cluster bootstrap and lifecycle on partner-provided bare metal, including NVIDIA GPU Operator and Network Operator.
•Oversee GPU isolation using technologies such as KubeVirt and VFIO and manage day-two infrastructure operations, upgrades, backup, recovery, and node replacement.
•Define managed inference architecture covering serving, multi-GPU and multi-node parallelism, autoscaling, request routing, endpoint reliability, and confidential-compute capabilities.
•Establish observability across the control plane, GPU fleet, and application layers through metrics, logging, alerting, and SLOs.
•Lead incident response, post-incident reviews, and the development of an on-call model that is sustainable for a lean engineering organization.
•Act as the primary technical interface with infrastructure partners and vendors, translating requirements into written specifications and acceptance tests.
•Manage technical escalations with partners through resolution and contribute to capacity planning and hardware sourcing decisions.
•Work directly with research, model-training, and product teams to translate workloads into platform requirements and manage capacity constraints.
•Hire and develop members of the platform team while maintaining a high technical bar.
•Contribute to architecture decisions involving distributed systems, high-performance computing, networking, storage, virtualization, and GPU workloads.

GPU InfrastructureTechnical LeadSlurmKubernetesHPCAI InfrastructureRemoteFull-timeBare MetalDistributed Systems

Key Responsibilities

•Own the end-to-end platform architecture, including architecture proposals, high-level and low-level designs, technical reviews, and ongoing architecture documentation.

•Lead and line-manage a distributed engineering team across backend, frontend, DevOps, QA, and documentation.

•Establish engineering standards, oversee code and design reviews, manage release gates, conduct one-to-ones, and provide growth and performance feedback.

•Design, build, and operate a managed Slurm service supporting research and model-training workloads.

•Own Slurm controllers, accounting, partitions, login nodes, node onboarding, acceptance testing, driver and CUDA baselines, upgrades, stalled-job detection, node health, draining, autohealing, storage visibility, identity, and workload isolation.

•Lead GPU infrastructure operations on bare-metal environments, including NVIDIA drivers, CUDA, Fabric Manager, NVSwitch, DCGM, MIG, node burn-in, and acceptance processes.

•Own Kubernetes cluster bootstrap and lifecycle on partner-provided bare metal, including NVIDIA GPU Operator and Network Operator.

•Oversee GPU isolation using technologies such as KubeVirt and VFIO and manage day-two infrastructure operations, upgrades, backup, recovery, and node replacement.

•Define managed inference architecture covering serving, multi-GPU and multi-node parallelism, autoscaling, request routing, endpoint reliability, and confidential-compute capabilities.

•Establish observability across the control plane, GPU fleet, and application layers through metrics, logging, alerting, and SLOs.

•Lead incident response, post-incident reviews, and the development of an on-call model that is sustainable for a lean engineering organization.

•Act as the primary technical interface with infrastructure partners and vendors, translating requirements into written specifications and acceptance tests.

•Manage technical escalations with partners through resolution and contribute to capacity planning and hardware sourcing decisions.

•Work directly with research, model-training, and product teams to translate workloads into platform requirements and manage capacity constraints.

•Hire and develop members of the platform team while maintaining a high technical bar.

•Contribute to architecture decisions involving distributed systems, high-performance computing, networking, storage, virtualization, and GPU workloads.