Technical Lead - GPU Infrastructure

The ideal candidate combines deep hands-on GPU and infrastructure expertise with proven technical leadership experience.

They should be comfortable operating complex production systems, making architecture decisions, leading distributed teams, and remaining close to the code and infrastructure.

• 8+ years of hands-on engineering experience, including at least 3 years leading teams responsible for infrastructure platforms used by other teams.
• Bachelor's or Master's degree in computer science, engineering, or a related field, or equivalent practical experience.
• Extensive hands-on experience operating Slurm in production, including slurmctld, slurmdbd, partitions, QoS, priority, accounting, prolog and epilog, node health, and upgrades.
• Experience operating HPC or GPU training clusters for research or model-development users.
• Strong experience operating NVIDIA GPU fleets on bare metal, including driver and CUDA lifecycles, Fabric Manager, NVSwitch, DCGM, MIG, node burn-in, and acceptance.
• Deep knowledge of InfiniBand, subnet configuration, RDMA, SR-IOV, and diagnosing multi-node NCCL performance issues.
• Strong Linux systems expertise, including kernel modules, drivers, PCIe passthrough, vfio-pci, cgroups, namespaces, and performance tuning.
• Proven production Kubernetes experience covering control planes, upgrades, CNI, CSI, operators, custom controllers, and multi-tenancy.
• Experience with HPC storage and large-scale data movement, including shared filesystems such as VAST, Lustre, or NFS and node-local NVMe caching.
• Experience distributing large model weights and datasets across multiple nodes.
• Strong observability and operations experience with Prometheus, Grafana, Loki, or comparable platforms, including SLOs, incident response, and post-incident reviews.
• Working proficiency in JavaScript and Node.js sufficient to review control-plane, CLI, and worker services and make architecture decisions.
• Experience delivering a multi-tenant IaaS, PaaS, research computing service, or comparable platform with resource isolation, quotas, usage metering, APIs, and CLI interfaces.
• Demonstrated people leadership across time zones and the ability to lead cross-functional technical reviews.
• Strong written architecture and decision-making skills, including documenting alternatives and trade-offs.
• Confidence communicating technical decisions and respectfully challenging partners or executives when necessary.
• Excellent written and spoken English.
• Fully remote availability with a working location between UTC and UTC+5:30 to provide overlap with teams and partners across Europe and India.
• Willingness to travel occasionally to partner sites and team events.

• Slurm operators on Kubernetes, such as Soperator or Slinky, or Kubernetes-native schedulers such as Kueue, Volcano, KAI, or Kubeflow Trainer.
• Modern model-serving technologies such as vLLM, SGLang, or TensorRT-LLM.
• GPU parallelism strategies, quantization trade-offs, and GPU memory planning.
• Multi-tenant GPU isolation using KubeVirt, Kata Containers, QEMU/KVM, Firecracker, or similar technologies.
• Confidential computing technologies such as Intel TDX, AMD SEV-SNP, or NVIDIA confidential-compute capabilities.
• Cluster API, kubeadm, Cilium, GPU autohealing, infrastructure as code, and GitOps.
• Experience working on the operator side of a GPU cloud, university or national HPC center, or AI research platform.
• Peer-to-peer or distributed-systems experience.
• Experience working with hardware providers responsible for provisioning but not operating infrastructure, including establishing contracts and acceptance tests.

• 100% remote position.
• Opportunity to lead the architecture and delivery of a sophisticated GPU infrastructure platform.
• High-impact technical leadership role spanning bare-metal GPU infrastructure, Slurm, Kubernetes, inference, and observability.
• Leadership responsibility for a distributed engineering organization across multiple technical disciplines.
• Significant ownership over architecture, engineering standards, delivery planning, and team development.
• Direct involvement with infrastructure partners and hardware providers.
• Opportunity to support advanced AI research, model training, and managed inference workloads.
• International and distributed working environment with colleagues and partners across Europe and India.
• Occasional opportunities for travel to partner locations and team events.
• Opportunity to work at the intersection of high-performance computing, AI infrastructure, distributed systems, and cloud-native technologies.

We use an

AI-powered matching process

to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements.

Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company.

The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

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