ML Ops DSX Senior Engineer

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

For our client, we are seeking a ML Ops DSX Senior Engineer to join the team of a leader in the Telecommunications & Hardware space. This role will focus on enabling scalable AI infrastructure and operational excellence for enterprise and strategic customers. You will partner closely with engineering teams and external stakeholders to deploy, support, and optimize large-scale machine learning workloads across modern cloud-native environments. The position combines hands‑on technical problem solving with architecture, implementation, and customer‑facing collaboration in a fast‑paced setting.

Location: Remote

Compensation: $184,000 – $356,500 annually

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, focusing on performance optimization
• Implement reference architectures and ensure smooth integrations with partner platforms
• Create detailed implementation guides and documentation for standard methodologies

Qualifications

• BS, MS, or Ph.D. in Computer Science, Computer/Electrical Engineering, or related field, or equivalent experience
• 8+ years in customer-facing technical roles like Solutions Engineering or ML Infrastructure Engineering, preferably in large-scale cloud environments
• Strong expertise in Linux, distributed computing, Kubernetes, containers, and GPU scheduling
• Demonstrated AI/ML experience with large-scale training and inference workloads in production
• Solid programming skills in Python/Go, with experience in frameworks like PyTorch or TensorFlow
• Ability to collaborate with engineering teams in fast-paced environments and resolve complex technical issues
• Excellent communication skills for articulating technical concepts to diverse audiences

Benefits

• Generous benefits package including equity options
• Opportunity to work with innovative and dedicated teams
• Potential for career growth due to rapid company expansion
• Recognition as one of the most desirable employers in tech

Our client is an equal opportunity employer. We encourage you to apply even if you don’t meet every qualification—your background could be exactly what this team needs.