Infrastructure Engineer

Acceler8 Talent; San Francisco (CA); On-site; USD 150,000 - 390,000; Full time

Job summary

Acceler8 Talent in San Francisco is seeking a Member of Technical Staff, Infrastructure to build and operate the cluster infrastructure behind our AI platform. You will determine how new accelerator hardware is brought online, how compute fleets are provisioned, and how production inference systems remain reliable at scale.

You will deploy production clusters across accelerator architectures, automate provisioning and fleet lifecycle, improve scheduling and resource utilization, and build

Qualifications

• Experience in infrastructure, platform engineering, cluster engineering, SRE, or HPC.
• Strong Linux systems knowledge and production debugging experience.
• Experience operating Kubernetes, Slurm, Nomad, or similar orchestration systems.
• Infrastructure automation experience using Python, Go, Terraform, or Ansible.
• Experience with GPU or accelerator infrastructure, including drivers, firmware, CUDA, or ROCm.
• A track record of building observable, recoverable, and reliable production systems.

Responsibilities

• Deploy production clusters across different accelerator architectures.
• Automate bare-metal provisioning, validation, upgrades, and fleet lifecycle management.
• Improve cluster scheduling, resource utilization, isolation, and capacity management.
• Build observability systems for faster debugging, incident response, and recovery.
• Make new accelerators production-ready across drivers, firmware, networking, and orchestration.
• Partner with runtime, compiler, distributed-systems, networking, and hardware engineers.

Skills

Linux systems

Production debugging

Observability

Cloud platforms

Tools

Kubernetes

Slurm

Nomad

Python

Go

Terraform

Ansible

CUDA

GPU drivers

Job description

Member of Technical Staff, Infrastructure

$150k to $390k base + equity

I’m working with a well-funded AI infrastructure startup (Series A) building a cloud platform that runs inference workloads across GPUs, CPUs, and emerging accelerator architectures.

The team is solving a difficult infrastructure problem: making new different hardware accelerators usable through one reliable platform, without requiring customers to redesign their software stack for every accelerator.

This role will build the cluster infrastructure behind that platform. You’ll determine how new hardware is brought online, how compute fleets are provisioned and operated, and how production inference systems remain reliable as they scale.

You’ll work on problems such as:

• Deploying production clusters across different accelerator architectures
• Automating bare-metal provisioning, validation, upgrades, and fleet lifecycle management
• Improving cluster scheduling, resource utilization, isolation, and capacity management
• Building observability systems for faster debugging, incident response, and recovery
• Making new accelerators production-ready across drivers, firmware, networking, and orchestration
• Partnering with runtime, compiler, distributed-systems, networking, and hardware engineers.

Looking for engineers who have:

• Experience in infrastructure, platform engineering, cluster engineering, SRE, or HPC
• Strong Linux systems knowledge and production debugging experience
• Experience operating Kubernetes, Slurm, Nomad, or similar orchestration systems
• Infrastructure automation experience using Python, Go, Terraform, or Ansible
• Experience with GPU or accelerator infrastructure, including drivers, firmware, CUDA, or ROCm
• A track record of building observable, recoverable, and reliable production systems

This is an opportunity to join a small, highly technical team and build production infrastructure across multiple generations and types of AI hardware. The strongest candidates will be able to explain what they personally built, operated, measured, and debugged at scale.