Technical Lead – GPU Infrastructure

🔥 18 hours ago

🇵🇰 Pakistan – Remote

⏰ Full Time

🟠 Senior

🧑‍💻 Full-stack Engineer

👻 Ghost score 25%

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Tether.to

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2014

₿ Crypto

💳 Fintech

💸 Finance

Crypto • Fintech • Finance

Tether. to is a leading digital asset company that pioneers the use of stablecoins in the blockchain space. As the most widely adopted stablecoin, Tether tokens are designed to be pegged 1-to-1 with fiat currencies, offering a stable digital asset option for users. The platform facilitates these token transactions across multiple blockchains, enhancing cross-border transactions while maintaining transparency with daily records of total assets and reserves. Tether's initiatives include educational programs promoting digital asset usage, especially targeting regions like the Middle East, Turkey, and the Philippines. Tether thus positions itself as a disruptor in the traditional financial system by enabling a stable, efficient method of handling transactions in the digital currency world.

📋 Description

• Own the end-to-end architecture of Tether Data's Cosmic AC GPU compute and managed inference platform • Create and maintain architecture proposals, high-level designs, and low-level designs through review • Lead and line-manage approximately twelve distributed engineers across backend, frontend, DevOps, QA, and documentation • Establish engineering standards, conduct code and design reviews, manage release gates, hold one-to-ones, and provide growth and performance input • Design, build, and operate a managed Slurm service for research users • Own Slurm controller and accounting, partitions, login nodes, node onboarding, driver and CUDA baselines, stalled-job and node-health detection, drain, autohealing, storage visibility, identity, and isolation • Own Kubernetes cluster bootstrap and lifecycle on partner-provided bare metal • Manage NVIDIA GPU Operator and Network Operator, VM-based GPU isolation using KubeVirt and VFIO, upgrades, backup and recovery, and node replacement • Define managed inference architecture, multi-GPU and multi-node parallelism, autoscaling, request routing, endpoint reliability, and confidential-compute-capable capacity • Establish metrics, logging, alerting, and SLOs across the control plane, GPU fleet, and application tiers • Lead incident response, post-incident reviews, and a sustainable on-call model • Serve as primary technical interface to infrastructure partners and vendors • Turn requirements into written specifications and acceptance tests, manage escalations, and provide input to capacity planning and hardware sourcing • Work with research, model-training, and product teams to translate workloads into platform requirements and broker capacity • Complete the platform team and set the technical bar for new engineers

🎯 Requirements

• Eight or more years of hands-on engineering experience • At least three years leading teams that build and operate infrastructure platforms other teams depend on • Bachelor's or Master's degree in computer science or engineering, or equivalent practical experience • Hands-on experience running Slurm at scale, including slurmctld, slurmdbd, partitions, QoS and priority, accounting, prolog and epilog, node health scripting, and upgrades with jobs on the system • Experience operating an HPC or GPU training cluster for a research population is ideally desired • Experience operating NVIDIA GPU fleets on bare metal, including NVIDIA driver and CUDA lifecycle, Fabric Manager, NVSwitch, DCGM, MIG, node burn-in, and acceptance • Experience with InfiniBand fabric and subnet configuration, RDMA, SR-IOV, and diagnosing multi-node NCCL performance problems • Deep Linux systems knowledge, including kernel modules and drivers, PCIe passthrough, vfio-pci, cgroups, namespaces, and performance tuning • Production Kubernetes operation, including control plane, upgrades, CNI, CSI, operators, custom controllers, and multi-tenancy design • Experience with HPC storage and data movement, including VAST, Lustre, NFS, node-local NVMe caching, and distributing large model weights and datasets • Experience with Prometheus, Grafana, Loki or equivalents, SLOs, incident response, and post-incident review • Working fluency in JavaScript and Node.js sufficient to review control-plane, CLI, and worker services and make architecture decisions • Experience shipping a platform with real users, such as a multi-tenant IaaS/PaaS or research computing service • People management across time zones, cross-track review, written architecture decisions, and ability to challenge partners or executives with reasons • Excellent written and spoken English • Fully remote location based between UTC and UTC+5:30 • Desirable experience with Slurm operators on Kubernetes or Kubernetes-native schedulers • Desirable experience with modern serving stacks such as vLLM, SGLang, and TensorRT-LLM • Desirable experience with VM and container isolation, confidential computing, Cluster API, kubeadm, Cilium, autohealing, infrastructure as code, and GitOps • Desirable experience in GPU cloud, HPC centre, AI lab platform, peer-to-peer, distributed systems, or hardware-provider operations

🏖️ Benefits

• Fully remote work arrangement • Occasional travel to partner sites and team events