Senior Backend Engineer: Kubernetes & Slurm Platform

Lightning-Ai; New York (NY); Hybrid; USD 180,000 - 250,000; Full time

Benefits offered by this job

Health coverage

Equity / RSUs

401(k) matching

Unlimited PTO

Hybrid work model

Winter break

Parental & family leave

Learning & development allowance

Wellness stipend

Sabbatical program

In-office meals

Job summary

Lightning AI is seeking a Senior Backend Engineer for the Managed Services team in the U.S. to build control planes, backend services, and automation that provisions, operates, and scales Kubernetes and Slurm clusters across a large GPU fleet.

You will turn raw GPU capacity into production-ready environments for model training, inference, and deployment, tackling distributed systems and cloud infrastructure challenges at scale.

Qualifications

• Significant professional experience designing, building, and operating production backend systems using Go or Python.
• Deep hands-on experience with Kubernetes or Slurm in large-scale production environments.
• Strong understanding of distributed systems and cloud-native architectures.
• Experience designing scalable backend services, APIs, and automation for infra or platform ops.
• Strong networking, storage, and cloud infra fundamentals.

Responsibilities

• Design, build, and operate backend services powering Lightning AI's managed infra platform.
• Develop control plane services to provision and manage Kubernetes and Slurm clusters at scale.
• Automate cluster lifecycle, workload scheduling, and platform operations.

Skills

Go or Python

Kubernetes or Slurm

Distributed systems

Backend services design

APIs & automation

Tools

Kubernetes

Slurm

Terraform

Helm

GitOps

Job description

Lightning AI is seeking a Senior Backend Engineer for the Managed Services team in the U.S. to build control planes, backend services, and automation that provisions, operates, and scales Kubernetes and Slurm clusters across a large GPU fleet.

You will turn raw GPU capacity into production-ready environments for model training, inference, and deployment, tackling distributed systems and cloud infrastructure challenges at scale.