AI Inference Cloud Infra Platform Engineer

Gimlet Labs, Inc.; San Francisco (CA); On-site; USD 150,000 - 390,000; Full time

Job summary

Gimlet Labs, Inc. in San Francisco, CA is seeking a Member of Technical Staff - Infrastructure to build production-ready AI cloud infrastructure across heterogeneous accelerator hardware. You will enable provisioning, orchestration, and reliability for a fast-moving AI platform.

The role requires strong Linux and cluster engineering skills, hands-on experience with Kubernetes and HPC schedulers, and proficiency in Python or Go for automation.

Qualifications

• Experience in infrastructure, cluster engineering, platform engineering, SRE, or HPC.
• Strong Linux systems knowledge and production debugging experience.
• Experience operating Kubernetes, Slurm, Nomad, or similar orchestration systems.
• Experience automating infrastructure with Python, Go, Terraform, Ansible, or similar tools.
• Experience with GPU or accelerator infrastructure, including drivers, firmware, or CUDA/ROCm.
• The ability to build systems that are observable, recoverable, and reliable in production.

Responsibilities

• Deploy and operate production clusters across different accelerator architectures.
• Automate hardware provisioning, validation, upgrades, and fleet lifecycle management.
• Improve cluster scheduling, resource utilization, isolation, and capacity management.
• Build observable infrastructure that enables faster debugging, incident response, and recovery.
• Partner across distributed systems, runtime, compiler, networking, and hardware teams to bring new accelerators into production.

Skills

Linux systems

Cluster engineering

SRE

Production debugging

Education

Bachelor’s degree in a relevant field

Tools

Kubernetes

Slurm

Nomad

Python

Go

Terraform

Ansible

Job description

Gimlet Labs, Inc. in San Francisco, CA is seeking a Member of Technical Staff - Infrastructure to build production-ready AI cloud infrastructure across heterogeneous accelerator hardware. You will enable provisioning, orchestration, and reliability for a fast-moving AI platform.

The role requires strong Linux and cluster engineering skills, hands-on experience with Kubernetes and HPC schedulers, and proficiency in Python or Go for automation.