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Browse remote Kubernetes positions — AI infrastructure, MLOps platforms, GPU cluster orchestration, and model serving at scale.
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Posted 7 hours ago
Research Engineer / Scientist, Pre-training
Anthropic
Remote-Friendly (Travel-Required) | San Francisco, CA | Seattle, WA | New York City, NY
Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society...
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AlphaSense is the AI platform redefining market intelligence and workflow orchestration, trusted by thousands of leading organizations to drive faster...
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Rebuild how the world works, to make institutions work better for the people they serve. About Brain Co. Brain Co. builds AI-native operating systems...
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AI has learned from almost everything online, but it still struggles with the physical world, where 99.5% of economic activity happens. It doesn’t kno...
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Turing’s mission is to accelerate superintelligence to drive real economic progress. Headquartered in San Francisco, Turing works with frontier AI lab...
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CodeRabbit is the leading AI code review platform, trusted by more than 17,000 customers and 150,000 open-source projects, conducting over 2 million c...
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CodeRabbit is the leading AI code review platform, trusted by more than 17,000 customers and 150,000 open-source projects, conducting over 2 million c...
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Posted 4 days ago
Software Engineering Manager (Agentic Engineering, US Eastern)
Coder
United States
As an Engineering Manager on Coder’s Agentic Engineering team, you’ll lead engineers building and evolving the systems behind our agentic development...
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We are looking for a Senior Solutions Engineer, Federal/Public Sector to join our Solutions Engineering team. You will partner with Account Executives...
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CodeRabbit is the leading AI code review platform, trusted by more than 17,000 customers and 150,000 open-source projects, conducting over 2 million c...
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Role guide
What to expect in remote Kubernetes jobs
Kubernetes is the standard infrastructure platform for production AI systems at scale. Remote Kubernetes jobs at AI companies involve GPU node pool management, MLOps platform engineering, and autoscaling model serving. These roles sit at the intersection of cloud infrastructure and ML systems.
What you'll work on
- Design Kubernetes clusters for distributed model training with GPU nodes
- Deploy AI models with autoscaling using KServe or Seldon
- Build the MLOps platform your data science team runs experiments on
- Manage resource quotas and scheduling for multi-team AI workloads
What companies look for
- GPU node pool configuration and scheduling experience
- Knowledge of Kubeflow, KServe, or Seldon for ML serving
- Helm chart development and GitOps deployment practices
Frequently asked questions
What Kubernetes roles are available remotely in AI?
Primarily MLOps platform engineering, AI infrastructure, and model serving roles. These positions require strong Kubernetes fundamentals plus ML-specific tooling knowledge (Kubeflow, KServe, GPU scheduling).
Do remote Kubernetes AI jobs require cloud provider experience?
Almost always. EKS (AWS), GKE (Google Cloud), and AKS (Azure) are the most common managed Kubernetes platforms. Most job listings specify a primary cloud provider.
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