Technical Lead – GPU Infrastructure

🔥 21 hours ago

🇺🇸 United States – Remote

⏰ Full Time

🟠 Senior

🧑‍💻 Full-stack Engineer

👻 Ghost score 25%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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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 platform architecture through architecture proposals, high-level and low-level designs, reviews, and maintenance of the baseline • Lead and line-manage approximately twelve distributed engineers across backend, frontend, DevOps, QA, and documentation • Set 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 controllers, 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 • Implement NVIDIA GPU Operator, Network Operator, VM-based GPU isolation with KubeVirt and VFIO, upgrades, backup and recovery, and node replacement • Design managed inference serving 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 • Translate requirements into written specifications and acceptance tests, manage escalations, and contribute 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 establish 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 with Slurm at scale, including slurmctld, slurmdbd, partitions, QoS, priority, accounting, prolog and epilog, node health scripting, and upgrades with jobs running • Experience operating HPC or GPU training clusters for research users is ideally preferred • NVIDIA driver and CUDA lifecycle experience • Fabric Manager and NVSwitch experience on SXM systems • DCGM-based health and utilization, MIG, node burn-in and acceptance experience • InfiniBand fabric and subnet configuration, RDMA, SR-IOV, and diagnosing multi-node NCCL performance problems • Linux 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 • HPC storage and data movement experience with VAST, Lustre, NFS, node-local NVMe caching, and distributing large model weights and datasets • Prometheus, Grafana, Loki or equivalents, SLOs, incident response, and post-incident review • Working fluency in JavaScript and Node.js sufficient to review and make architecture decisions • Experience with a shipped platform used by real users, such as multi-tenant IaaS, PaaS, or research computing service • People management across time zones, cross-track review, written architecture decisions, and partner/executive communication • Excellent written and spoken English • Desirable experience with Soperator, Slinky, Kueue, Volcano, KAI, Kubeflow Trainer, vLLM, SGLang, TensorRT-LLM, KubeVirt, Kata Containers, QEMU, KVM, Firecracker, confidential computing, Cluster API, kubeadm, Cilium, NVSentinel-class autohealing, infrastructure as code, GitOps, GPU cloud/HPC/AI lab platforms, distributed systems, and hardware-provider contracts

🏖️ Benefits

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