Remote ML Ops Senior Engineer — Enterprise AI Infrastructure

Ladders; United States; Remote; USD 184,000 - 357,000; Full time

Benefits offered by this job

Equity options

Remote work

Career growth

Job summary

Ladders is seeking an ML Ops DSX Senior Engineer to join a leader in Telecommunications & Hardware, focusing on scalable AI infrastructure and enterprise workloads. This remote role partners with engineering teams and customers to deploy, support, and optimize large-scale ML workloads across cloud-native environments.

The candidate will work hands-on with architecture, implementation, and customer-facing collaboration in a fast-paced setting, requiring strong Linux, Kubernetes, and GPU

Qualifications

• 8+ years in customer-facing technical roles in large-scale cloud environments.
• BS/MS/PhD in CS/EE or related field, or equivalent experience.
• Strong Linux, distributed computing, Kubernetes, containers, and GPU scheduling.
• Experience with large-scale training/inference workloads in production.

Responsibilities

• Develop innovative solutions to enhance AI infrastructure capabilities.
• Build and deploy custom AI solutions on cloud platforms.
• Act as the main technical contact for strategic customers, providing support and troubleshooting.
• Deploy and manage AI workloads across various environments using Kubernetes and containers.
• Profile and tune large-scale training and inference workloads for performance.
• Create implementation guides and documentation for standard methodologies.

Skills

Customer-facing experience

Excellent communication

Collaborative skills

Education

BS/MS/PhD in CS/EE or related field

Tools

Linux

Kubernetes

Containers

GPU scheduling

Python

Go

PyTorch

TensorFlow

Job description

Ladders is seeking an ML Ops DSX Senior Engineer to join a leader in Telecommunications & Hardware, focusing on scalable AI infrastructure and enterprise workloads. This remote role partners with engineering teams and customers to deploy, support, and optimize large-scale ML workloads across cloud-native environments.

The candidate will work hands-on with architecture, implementation, and customer-facing collaboration in a fast-paced setting, requiring strong Linux, Kubernetes, and GPU