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Browse vetted remote Kubernetes positions — AI infrastructure, MLOps platforms, GPU cluster orchestration, and model serving at scale.
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Posted 1 week ago
Infrastructure Engineer
Dataiku is the Platform for AI Success, the enterprise orchestration layer for building, deploying, and governing AI. In a single environment, teams d...
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Posted 1 week ago
Fullstack Software Engineer - Core
Dataiku is the Platform for AI Success, the enterprise orchestration layer for building, deploying, and governing AI. In a single environment, teams d...
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Posted 1 week ago
Senior Product Security Engineer
About AlphaSense: The world’s most sophisticated companies rely on AlphaSense to remove uncertainty from decision-making. With market intelligence an...
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Posted 1 week ago
Senior Partner Agent Architect (UK)
About Parloa Parloa’s mission is to make every customer conversation feel effortless for both customers and the companies serving them. As agentic AI...
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Posted 1 week ago
Senior Cloud Security Engineer
About AlphaSense: The world’s most sophisticated companies rely on AlphaSense to remove uncertainty from decision-making. With market intelligence an...
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Posted 1 week ago
Senior Software Engineer, AI/ML Platform
Agility’s commercially deployed humanoids operate alongside teams in warehouses, manufacturing facilities, and distribution centers—tackling physicall...
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Posted 1 week ago
Manager, Forward Deployed Engineering
About Parloa Parloa’s mission is to make every customer conversation feel effortless for both customers and the companies serving them. As agentic AI...
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Posted 1 week ago
DevOps Engineer
About Arize AI is rapidly transforming the world. As generative AI reshapes industries, teams need powerful ways to monitor, troubleshoot, and optimiz...
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Posted 1 week ago
Director of Customer Experience
Who We Are Lightning AI is the company behind PyTorch Lightning. Founded in 2019, we build an end-to-end platform for developing, training, and deploy...
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Posted 1 week ago
Associate Director, MLOps Engineering
PathAI's mission is to improve patient outcomes with AI-powered pathology. Our platform promises substantial improvements to the accuracy of diagnosi...
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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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