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Principal Technical Program Manager leading GPU infrastructure strategy and execution, aligning engineering, operations, and partners to scale data-center and distributed systems.
Leads the architecture and reliability of high-performance Ethernet front-end networks for AI GPU clusters, focusing on Arista, Nokia, and Nvidia Cumulus platforms to support inference, storage, and long-haul traffic.
Principal Network Engineer in Singapore to lead APAC network infrastructure for a cloud built for AI, focusing on scalability, reliability, and automation across data centers.
Principal Systems Engineer in Singapore manages and operates bare-metal cloud infrastructure for AI workloads, handling hardware lifecycle, firmware, networking, and OS deployment across large-scale data centers.
A senior technical authority owning Nscale's high-performance Ethernet front-end networks for AI GPU clusters — designing large-scale leaf-spine/Clos fabrics on NVIDIA Cumulus, Arista EOS, and Nokia platforms, handling DCI/long-haul optics and storage networking, and driving reliability and incident improvements across teams.
Principal Network Engineer designs and maintains high-speed Infiniband/RDMA fabrics for an AI-focused GPU cloud, ensuring reliability and performance for AI workloads.
Build and maintain the control plane and automation for AI/HPC infrastructure, including GPU fleet provisioning, network operations, and observability to improve system availability and reduce costs.
Principal Network Engineer designing and operating Nscale's AI-optimised network infrastructure—InfiniBand, RoCE, and high-performance Ethernet fabrics—using Python, Ansible, and IaC tooling in UK data centres.
Oversees AI/HPC infrastructure operations, ensuring GPU fleet and network stability at scale; bridges engineering and business goals via program leadership, metrics, and cross-functional coordination.
A principal-level engineer on Nscale's AI Infrastructure Operations team who defines how multi-thousand-GPU AI clusters are validated and tuned for production: building Python control-plane and burn-in tooling, running real distributed training/inference workloads, and debugging performance across GPUs, InfiniBand/RoCE fabrics, storage, and SLURM/Kubernetes schedulers.
Principal-level engineer to own the multi-year roadmap and architecture for Nscale's inference, evals, and post-training platform on its GPU cloud, setting standards that 20-50+ engineers build to. Core work spans GPU kernel efficiency, serving/KV cache architecture, quantization, benchmarking/evals, and RL infrastructure. Salary $290k-$443k USD plus bonus and equity.
Technical lead for Nscale's Fleet Manager platform, building Python-based workflow automation that provisions, tests, monitors, and self-heals GPU nodes and network switches across its GenAI cloud. Owns architecture and delivery, mentors senior engineers, and integrates tooling like NetBox, OpenStack, MAAS, and Ironic.
A customer-facing principal architect at Nscale designs end-to-end GPU infrastructure for demanding AI workloads — GPU cluster and rack design, InfiniBand/RoCE networking, storage, and Kubernetes/Slurm orchestration — plus performance benchmarking with NCCL/DCGM and AI-driven automation using Terraform, Ansible, Python, and Go.
Nscale is hiring a Principal Technical Product Manager to own its GPU and compute hardware portfolio for large-scale AI workloads: defining multi-generation hardware roadmaps, leading NPI with OEM/ODM partners, building TCO models, and shaping internal deployment automation tooling. Requires 8+ years in technical/hardware product management and deep data-center infrastructure expertise.
Principal-level role owning the architecture and roadmap of Nscale's observability platform for its GPU cloud: metrics, logs, traces, and alerting at scale. Day to day you set technical strategy, build with tools like Prometheus, Grafana, OpenTelemetry, and ClickHouse on Kubernetes, drive postmortems, and mentor the observability team.
Senior individual-contributor program manager who drives Nscale's AI data center and physical infrastructure deployments: owning RFx processes, aligning internal teams with data center/construction partners, removing blockers, and setting delivery standards. Requires deep technical program management experience across data center, networking, or AI/HPC infrastructure.
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