Infrastructure Engineer

Acceler8 Talent; San Francisco, Northern (CA, KY); Hybrid; USD 150,000 - 390,000; Full time

Benefits offered by this job

Equity

Job summary

Acceler8 Talent is building a cloud platform that runs inference workloads across GPUs, CPUs, and accelerators. You’ll design how new hardware is brought online and how compute fleets are provisioned and operated.

This role focuses on production-ready infrastructure with observability, reliable scheduling, and close collaboration with runtime, compiler, distributed-systems, networking, and hardware engineers.

Qualifications

• Experience in building and operating large-scale infrastructure
• Strong Linux systems knowledge and production debugging experience
• Experience operating Kubernetes, Slurm, Nomad, or similar systems
• Infrastructure automation experience using Python, Go, Terraform, or Ansible
• Experience with GPU/accelerator infrastructure, including drivers, firmware, CUDA, or ROCm
• Track record of observable, recoverable, and reliable production systems

Responsibilities

• Build the cluster infrastructure behind the AI inference platform
• Determine how new hardware is brought online and how compute fleets are provisioned
• Improve cluster scheduling, resource utilization, isolation, and capacity management
• Develop observability systems for faster debugging and recovery
• Make accelerators production-ready across drivers, firmware, networking, and orchestration
• Collaborate with runtime, compiler, distributed-systems, networking, and hardware engineers

Skills

Infrastructure

Platform engineering

SRE

HPC

Linux systems knowledge

Production debugging

Observability

Cluster engineering

Tools

Kubernetes

Slurm

Nomad

Python

Go

Terraform

Ansible

CUDA

ROCm

Job description

Member of Technical Staff, Infrastructure

On-site | San Francisco, CA | 5 days per week

$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.