NVIDIAHiring

ML Ops DSX Senior Engineer

  • Remote, United States
  • Senior
  • Posted 5 days ago
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In one line

Customer-facing engineering role helping strategic partners run large AI training and inference workloads on GPU cloud platforms, with remote and on-site support.

About the role

The DSX team works directly with AI companies on distributed systems and performance problems, and this role is the main technical contact for key cloud partners.

At a glance

Pay
Not stated in the posting
Remote scope
Remote, United States
Seniority
Senior
Skills
Kubernetes, Linux, Python, Go, PyTorch, TensorFlow, MLOps, GPU scheduling
Posted
30 Oct 2026
Last checked
2 Nov 2026

What you will do

  • Build and deploy AI solutions on partner cloud platforms, covering distributed training, inference tuning and MLOps pipelines
  • Is main technical contact for strategic partners, troubleshooting production issues and guiding their engineers
  • Run AI workloads across GPU cloud, partner data centers and major cloud providers using Kubernetes and containers
  • Profile and tune large-scale training and inference, with observability and SLO/SLA monitoring to cut latency and cost
  • Write implementation guides, runbooks and post-mortems that codify how to run AI workloads at scale

What they look for

  • BS, MS or PhD in computer science, computer or electrical engineering, or equivalent experience
  • Strong Linux, distributed computing, Kubernetes, container and GPU scheduling skills on multi-tenant platforms
  • Production experience with large training and inference workloads such as LLMs or recommendation systems
  • Python or Go programming plus hands-on PyTorch or TensorFlow for training and serving

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Questions about this job

Is this job fully remote, and from where?

The posting lists it as remote for people in the United States.

What does this job pay?

The posting does not state a salary, so there is no figure to quote.

What experience is needed?

It is a senior role. The posting asks for: BS, MS or PhD in computer science, computer or electrical engineering, or equivalent experience; strong Linux, distributed computing, Kubernetes, container and GPU scheduling skills on multi-tenant platforms; production experience with large training and inference workloads such as LLMs or recommendation systems. Skills it names include Kubernetes, Linux, Python, Go.

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