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Inferact

Open-source LLM inference engine with scalability

Production Site Reliability Engineer

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

About the Job

Requirements

  • Bachelor's degree or equivalent experience in computer science, engineering, systems, infrastructure, or a similar field.
  • Hands-on experience building or operating production distributed systems, cloud infrastructure, or services where reliability has meaningful user or business impact.
  • Strong programming or scripting ability in Python, Go, Bash, or a similar language, with experience building tools and automation that solve operational problems.
  • Experience responding to significant production incidents, including mitigation, root cause analysis, escalation, and following corrective actions through to completion.
  • Practical understanding of service-level objectives, service-level indicators, error budgets, and alerting that distinguishes user-impacting failures from noise.
  • Strong Linux, networking, and systems-debugging fundamentals, with the ability to investigate failures across application and infrastructure boundaries.
  • Experience supporting AI inference, model serving, machine-learning infrastructure, GPU workloads, or other latency-sensitive, high-throughput services is preferred.
  • Experience deploying, operating, and debugging Kubernetes-based services, with familiarity with Docker, Terraform, or comparable infrastructure tooling is preferred.
  • Experience building observability with metrics, logs, traces, dashboards, and actionable alerts across distributed systems is preferred.
  • Experience improving deployment safety and recovery through automation, testing, release gates, and rollback procedures is preferred.
  • Experience with resource scheduling, capacity planning, or workload isolation in multi-tenant cloud or GPU environments is preferred.

Responsibilities

  • Help own reliability across the production lifecycle, from release safety and observability to incident response and recovery.
  • Define service-level objectives around availability, latency, and successful inference requests.
  • Build monitoring that helps engineers pinpoint failures and automate recurring operational work.
  • Drive incident mitigation, coordinate escalation, and lead post-mortems that result in concrete prevention work.
  • Partner with engineering on capacity planning and operational readiness so the platform can grow without becoming harder to operate.
  • Own reliability for an inference service or other mission-critical platform through significant growth, as applicable.
  • Build automation that measurably reduces operational toil, improves recovery time, or prevents repeat incidents, as applicable.
  • Establish practical service-level objectives, post-mortem processes, or production-readiness reviews that help engineering teams ship with greater confidence, as applicable.

Desired Qualifications

  • Experience supporting AI inference, model serving, machine-learning infrastructure, GPU workloads, or other latency-sensitive, high-throughput services.
  • Experience deploying, operating, and debugging Kubernetes-based services, with familiarity with Docker, Terraform, or comparable infrastructure tooling.
  • Experience building observability with metrics, logs, traces, dashboards, and actionable alerts across distributed systems.
  • Experience improving deployment safety and recovery through automation, testing, release gates, and rollback procedures.
  • Experience with resource scheduling, capacity planning, or workload isolation in multi-tenant cloud or GPU environments.
  • Having owned reliability for an inference service or other mission-critical platform through a period of significant growth.
  • Having built automation that measurably reduced operational toil, improved recovery time, or prevented repeat incidents.
  • Having established practical service-level objectives, post-mortem processes, or production-readiness reviews that helped engineering teams ship with greater confidence.

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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