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About the Role

Inferact is seeking a hands-on cluster administration engineer to manage and operate high-performance GPU compute infrastructure, ensuring its health, availability, observability, and usability for engineering teams. This role involves taking ownership of cluster health, GPU availability, monitoring, alerting, scheduling, access, diagnostics, and incident response, while collaborating with leadership to standardize compute operations across providers.

Responsibilities

Own and operate high-performance GPU compute infrastructure.
Ensure infrastructure is healthy, available, observable, and usable around the clock.
Take ownership of cluster health, GPU availability, monitoring, alerting, scheduling, access, diagnostics, and incident response.
Work closely with engineering leadership and infrastructure owners to standardize compute provisioning, operation, debugging, and scaling across providers.
Automate operational workflows using Bash, Python, Ansible, Terraform, Helm, or similar tooling.
Improve cluster utilization, reduce idle or unavailable GPU capacity, and debug scheduling or resource contention issues.
Manage secure access, identity, permissions, SSH, VPNs, bastion hosts, secrets, and basic infrastructure security hygiene.
Build monitoring, alerting, runbooks, health checks, or remediation workflows.
Standardize provisioning, diagnostics, monitoring, and operating patterns across multiple compute providers.

Requirements

Bachelor's degree or equivalent experience in computer science, engineering, systems administration, or similar.
Hands-on experience administering large compute clusters, HPC environments, university or research clusters, supercomputing systems, or production GPU clusters.
Strong Linux systems administration fundamentals across networking, processes, storage, package management, shell scripting, logs, access control, and system debugging.
Experience operating GPU servers, including driver management, GPU health monitoring, node failures, memory errors, scheduler issues, and hardware diagnostics.
Experience with cluster scheduling and resource allocation using SLURM, Kubernetes, or equivalent tooling.
Ability to own urgent infrastructure incidents end-to-end when compute issues are blocking engineering teams.
Experience operating GPU compute across providers such as Lambda, CoreWeave, Crusoe, Nebius, Together, Fireworks, RunPod, or similar environments.
Familiarity with high-performance GPU networking such as InfiniBand, RoCE, NVLink / NVSwitch, RDMA, NCCL, or equivalent systems.
Experience with storage for HPC or ML workloads, including NFS, Lustre, Ceph, distributed filesystems, or other high-throughput storage systems.
Managed GPU or HPC infrastructure in a university lab, national lab, research institution, AI infrastructure company, hedge fund, HFT firm, or large-scale ML platform team.
Operated Kubernetes clusters for ML or GPU workloads at meaningful scale.
Carried real operational responsibility for infrastructure used by many engineers or researchers.

Skills

Linux systems administration
Networking
Processes
Storage
Package management
Shell scripting
Logs
Access control
System debugging
GPU server operation
Driver management
GPU health monitoring
Node failure diagnostics
Memory error diagnostics
Scheduler issue diagnostics
Hardware diagnostics
SLURM
Kubernetes
Bash
Python
Ansible
Terraform
Helm
InfiniBand
RoCE
NVLink / NVSwitch
RDMA
NCCL
NFS
Lustre
Ceph
Distributed filesystems
High-throughput storage systems
Secure access management
Identity management
Permissions management
SSH
VPNs
Bastion hosts
Secrets management
Infrastructure security hygiene
Monitoring
Alerting
Runbooks
Health checks
Remediation workflows

Location

San Francisco, California
Remote in the US

Work Type

Full-time

Experience Level

Hands-on experience administering large compute clusters
Experience operating GPU servers
Experience with cluster scheduling and resource allocation
Experience operating GPU compute across providers
Familiarity with high-performance GPU networking
Experience with storage for HPC or ML workloads
Managed GPU or HPC infrastructure
Operated Kubernetes clusters for ML or GPU workloads at meaningful scale
Carried real operational responsibility for infrastructure

Education Level

Bachelor's degree or equivalent experience in computer science, engineering, systems administration, or similar.

Salary/Compensations

$200,000 - $400,000 USD + equity

Benefits

Generous health, dental, and vision benefits
401(k) company match

About the Company

Inferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster.
Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware—a position that took years to build.

Equal Opportunity

We sponsor visas on a case-by-case basis.