Staff GPU Compute & Infra Automation Engineer (Equity)

ATOMS Careers page; San Francisco (CA); On-site; USD 224,000 - 284,000; Full time

Benefits offered by this job

Medical, Dental, Vision, Disability, and Life Insurance

Flexible Spending Account / Health Savings Account Options

401(k)

Equity

Sick Time and Unlimited Flexible Time Off

Team lunch every Tuesday and Thursday

Job summary

Atoms is seeking a skilled professional to manage GPU training clusters in their San Francisco office. The ideal candidate will have over 6 years of experience with Kubernetes and GPU operations, alongside strong programming in Python. The role demands a focus on automation and reliability while working onsite five days a week.

Compensation ranges from $224,000 to $284,000 per year, with additional benefits including health insurance, 401(k), and equity options.

Qualifications

• 6+ years of experience operating GPU compute on Kubernetes, with judgment to scale as demand grows.
• Strong programming and scripting skills in Python, Go, or similar.
• Familiarity with Infrastructure-as-Code tools such as Terraform or CloudFormation.
• Comfort with bare-metal Linux environments, GPU hardware, and networking.
• A bias toward automation, reliability, and operating critical systems well.

Responsibilities

• Manage and automate GPU training clusters, including provisioning and lifecycle management.
• Automate bare-metal bring-up to quickly and reliably add new machines.
• Build software abstractions for training and simulation workloads.
• Work at the hardware/software boundary to ensure speed and reliability.
• Diagnose and resolve issues quickly during day-to-day operations.
• Design infrastructure to scale from a smaller cluster to a larger fleet.

Skills

GPU compute operations

Python

Kubernetes

Infrastructure-as-Code tools

Linux environments

Job description

Atoms is seeking a skilled professional to manage GPU training clusters in their San Francisco office. The ideal candidate will have over 6 years of experience with Kubernetes and GPU operations, alongside strong programming in Python. The role demands a focus on automation and reliability while working onsite five days a week.

Compensation ranges from $224,000 to $284,000 per year, with additional benefits including health insurance, 401(k), and equity options.