Remote positions
Browse vetted remote Kubernetes positions — AI infrastructure, MLOps platforms, GPU cluster orchestration, and model serving at scale.
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Full-time roles and fixed-scope freelance missions — matched to your AI skills.
We're building the observability layer that on-call engineers actually want to use at 3am. Right now, too much of our incident triage still relies on...
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As a fully async team spread across nine time zones producing live sports content, we believe great engineers ship automation that quietly removes bot...
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We just crossed 400 model endpoints running across our customers' Kubernetes clusters, and right now the only way we find out one is degrading is when...
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PyTorch, real-time computer vision, and Kubernetes-orchestrated inference are the backbone of what we run on factory floors across three continents. D...
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Spread across nine time zones and fully async, our team still expects reliable answers fast when cloud spend spikes unexpectedly. Our observability sp...
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As a fully async team spread across nine time zones, we believe great engineers don't need to sit in the same room to build reliable evaluation infras...
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Our mission is zero-surprise deployments for every model our clients push to production. Docker, Kubernetes, MLflow, and Airflow are the core of what...
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We just closed a wave of new enterprise logos and our sales engineers are now spending more time spinning up demo environments than actually running d...
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Our deployment pipelines are growing faster than our confidence in them. Every new model version ships with some risk, and right now we only find out...
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We just closed our Series A and are scaling fast, which means the number of teams pushing model updates every week has tripled — and so has the risk o...
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Role guide
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 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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