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Inferact

Open-source LLM inference engine with scalability

Member of Technical Staff - Supercomputing Infrastructure

Site Reliability EngineeringConfirmed live in the last 24 hours
Full-timeSingapore

About the Job

Requirements

  • A bachelor's degree or equivalent experience in computer science, engineering, systems administration, or a similar field.
  • 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.
  • Ability to automate operational workflows using Bash, Python, Ansible, Terraform, Helm, or similar tooling.

Responsibilities

  • Own and operate the high-performance compute infrastructure that keeps engineering productive.
  • Maintain the health, availability, observability, and usability of GPU and HPC clusters across neo-cloud and dedicated compute providers and heterogeneous NVIDIA GPU, AMD GPU, and Google TPU hardware.
  • Own cluster health, accelerator availability, monitoring, alerting, scheduling, access, diagnostics, and incident response across systems used by engineers.
  • Work with engineering leadership and infrastructure owners to standardize how compute is provisioned, operated, debugged, and scaled across providers and hardware platforms.

Desired Qualifications

  • Experience managing 1,000 or more GPUs in production.
  • Experience operating GPU compute across providers such as Lambda, CoreWeave, Crusoe, Nebius, Together, Fireworks, RunPod, or similar environments.
  • Experience improving cluster utilization, reducing idle or unavailable GPU capacity, and debugging scheduling or resource contention issues.
  • Familiarity with high-performance GPU networking such as InfiniBand, RoCE, NVLink or 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.
  • Experience managing secure access, identity, permissions, SSH, VPNs, bastion hosts, secrets, and basic infrastructure security hygiene.
  • Background in research computing, scientific computing, ML infrastructure, SRE, platform engineering, or infrastructure operations for engineering-heavy teams.
  • Experience managing 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.
  • Experience building monitoring, alerting, runbooks, health checks, or remediation workflows that materially reduced operational toil or incident resolution time.
  • Experience operating Kubernetes clusters for ML or GPU workloads at meaningful scale.
  • Experience standardizing provisioning, diagnostics, monitoring, and operating patterns across multiple compute providers.
  • Experience carrying real operational responsibility for infrastructure used by many engineers or researchers.

About the Company

Inferact

Open-source LLM inference engine with scalability

Inferact builds AI inference infrastructure centered on vLLM, an open-source large language model inference engine. It offers a managed inference service for enterprises with serverless deployment, observability, troubleshooting, disaster recovery, and multi-tenant Kubernetes scaling. vLLM uses PagedAttention to optimize GPU memory, lowering costs and latency while preserving model quality, and Inferact supports new architectures and hardware under vendor-neutral governance with PyTorch Foundation. The goal is to commoditize AI inference and deliver a production-ready operating layer for the AI stack.

Company size
11-50
Company stage
Seed
Total funding
$150M
Headquarters
San Francisco, CA
Founded
2025

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