Tech Lead Manager, GPU Cluster Infrastructure
J
JobgetherAI infrastructure
Listing location: US; based in the United States., Working-hour overlap with Berkeley preferred.Full-TimeManager
Salary225,000 - 325,000 USD per year
Apply NowOpens the employer's application page
Job Details
- Experience
- At least five years of experience in systems or infrastructure engineering within production Linux environments.
- Required Skills
- PythonKubernetesGoPrometheusLinuxTerraform
Requirements
- At least five years of systems or infrastructure engineering experience in production Linux environments, particularly GPU infrastructure, HPC, or large-scale batch platforms.
- Experience leading engineers as a manager, technical lead, or project lead, including setting technical direction, defining scope, prioritizing work, and providing feedback.
- Demonstrated ownership of production systems from initial design through deployment, operation, and continuous improvement.
- Deep expertise in at least one of workload scheduling, distributed storage, networking, infrastructure security, or GPU systems, with breadth to review designs across other areas.
- Hands-on experience operating production Kubernetes environments for GPU workloads with a batch scheduler such as Slurm, Kueue, or Volcano, including quotas, prioritization, preemption, and node health management.
- Experience managing production infrastructure as code and observability 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++, and experience maintaining shared automation and services.
- Strong written and verbal communication skills, including preparing technical roadmaps, design documents, incident reports, and escalations.
- An interest in managing people is essential; previous direct people-management experience is not required.
- Advantageous experience includes distributed training infrastructure, multi-node PyTorch and NCCL debugging, NVIDIA drivers, GPU Operator, DCGM, InfiniBand or RoCE, and topology-aware workload placement.
- Advantageous experience includes distributed storage, cluster security and sandboxed runtimes, Kubernetes scheduler internals, multi-provider platforms, or establishing infrastructure teams and operational practices.
Responsibilities
- Define the platform’s technical direction, architecture, and roadmap with research teams and engineering stakeholders.
- Design and oversee GPU cluster scheduling, storage, networking, and resource allocation across multiple infrastructure providers.
- Contribute hands-on to architecture decisions, implementation, troubleshooting, and resolution of complex infrastructure failures.
- Recruit, mentor, and develop infrastructure engineers; establish ownership, priorities, project scopes, and feedback practices.
- Lead security strategy for shared GPU infrastructure, including identity and access management, workload isolation, admission controls, and sandboxing.
- Establish and improve observability, incident response, fault tolerance, postmortems, and on-call practices.
- Monitor and improve platform performance using GPU utilization, queue wait times, failure rates, and workload reliability metrics.
- Serve as the primary escalation point for research teams and infrastructure providers, converting recurring challenges into platform improvements.
- Collaborate with researchers and engineers to adapt infrastructure to changing research needs and experiments.
View Full Description & ApplyYou'll be redirected to the employer's site
