Tech Lead Manager, GPU Cluster Infrastructure

We are seeking an experienced technical leader to shape and scale the infrastructure powering cutting-edge AI research. This role combines hands-on systems engineering with team leadership, offering the opportunity to influence the architecture, reliability, performance, and security of a rapidly expanding GPU cluster platform. You will oversee infrastructure spanning multiple providers, including compute, networking, storage, scheduling, and automation. Working closely with researchers and engineers, you will ensure large-scale experiments run efficiently and reliably. You will also establish technical standards, strengthen operational practices, and build a high-performing infrastructure team. This is an opportunity to make foundational engineering decisions in a fast-moving research environment where infrastructure directly enables scientific progress.

• Experience leading engineers as a manager, technical lead, or project lead, including setting technical direction, defining project scope, prioritizing work, and providing constructive feedback. Previous direct people-management experience is not required, but a strong interest in managing people is essential.

• At least five years of experience in systems or infrastructure engineering within production Linux environments, particularly GPU infrastructure, high-performance computing (HPC), or large-scale batch platforms.

• Demonstrated ownership of production systems from initial design through deployment, operation, and continuous improvement.

• Deep expertise in at least one of the following areas: workload scheduling, distributed storage, networking, infrastructure security, or GPU systems, alongside sufficient breadth to review designs across the other areas.

• Hands-on experience operating production Kubernetes environments for GPU workloads with a batch scheduling layer such as Slurm, Kueue, Volcano, or an equivalent, including quotas, prioritization, preemption, and node health management.

• Experience managing infrastructure as code and observability for production environments using tools such as Terraform, Ansible, Helm, ArgoCD, and Prometheus, or equivalent technologies.

• Strong programming skills in at least one infrastructure-oriented language, such as Python, Go, Rust, or C++, with experience maintaining shared automation and services.

• Excellent written and verbal communication skills, including the ability to prepare technical roadmaps, design documents, incident reports, and clear escalations for engineers, researchers, and providers.

Additional skills that would be an advantage:

• Experience with distributed training infrastructure, including multi-node PyTorch and NCCL debugging, NVIDIA drivers, GPU Operator, DCGM, InfiniBand or RoCE networking, and topology-aware workload placement.

• Familiarity with distributed storage technologies such as VAST, Weka, Lustre, Ceph, or large-scale object storage, including checkpoint I/O optimization.

• Expertise in cluster security, including RBAC, admission control, node and container hardening, sandboxed runtimes such as gVisor, Kata, or Firecracker, and the isolation of autonomous agents on shared infrastructure.

• Knowledge of Kubernetes scheduler internals, custom controllers, gang scheduling, fair-share allocation, quotas, and the trade-offs between utilization, fairness, and latency.

• Experience building multi-provider platforms that unify scheduling and storage across separate clusters, including environments with limited network connectivity or uneven data locality.

• Previous experience establishing a new infrastructure team, hiring senior engineers, and implementing operational practices for incident management, on-call coverage, and technical reviews.

Candidates who meet most, but not necessarily all, of these requirements are encouraged to apply.