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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 2 months ago
Security Engineer, Infrastructure Security
About the TeamSecurity is at the foundation of OpenAI’s mission to ensure that artificial general intelligence benefits all of humanity.The Security t...
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Posted 2 months ago
Research Engineer/Research Scientist, Pre-training
About Anthropic Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users...
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Posted 2 months ago
Software Engineer, Infrastructure Security
About the TeamSecurity is at the foundation of OpenAI’s mission to ensure that artificial general intelligence benefits all of humanity.The Security t...
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Posted 2 months ago
Principal Security Engineer, Infrastructure Security
About the TeamSecurity is at the foundation of OpenAI’s mission to ensure that artificial general intelligence benefits all of humanity.The Security t...
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Posted 2 months ago
Principal Software Engineer, Infrastructure Security
About the TeamSecurity is at the foundation of OpenAI’s mission to ensure that artificial general intelligence benefits all of humanity.The Security t...
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Posted 1 week ago
Senior AI Inference Latency Reliability Engineer
United States — Los Angeles (UTC-7)
Kubernetes, Triton-style model servers, AWS GPU fleets and a lot of tracing data: that's the surface you'll be responsible for. Ulvex Systems runs an...
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Our customers spend more on GPUs than on everything else in their cloud bill combined, and roughly a third of that capacity sits idle. Kolvex Cloud al...
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You'd be the newest of six people on our release engineering squad, and you'd pair with a senior engineer for your first two months. Yalvex AI runs de...
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We just signed two customers whose traffic is spikier than anything we've handled. GPU nodes take minutes to warm up, and our current autoscaler react...
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Kubernetes, gRPC, and a mesh of interconnects across three cloud providers are what keep our training jobs talking to each other without stalling. If...
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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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