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.
The models you'll ship will directly decide how much every customer on our platform pays for inference next month, so accuracy here isn't optional. Y...
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Posted 1 month ago
United States — Los Angeles (UTC-7)
You'll be the sixth engineer on a team of 20, working directly with our LLM platform leads on the layer that keeps multi-agent workflows from silently...
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Our model deployment pipeline still requires someone to manually check a checklist before every release, and that checklist keeps growing every time s...
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We don't care where you work from most days, but we do want you in the room for our weekly incident review, because that's where the best monitoring i...
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MLflow, Kubernetes, and a growing fleet of feature pipelines are what you'll be working with daily. If you want infrastructure work that's actually ab...
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We serve QA teams at companies who can't afford flaky test infrastructure to slow down their release cycles, and right now our own internal test envir...
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We just closed our Series A and our serverless ML platform is onboarding new customers faster than our deployment tooling can comfortably handle. We n...
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As a fully async team spread across 9 time zones, we believe great engineers do their best work without someone watching over their shoulder. This rol...
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Docker, Kubernetes, and MLflow are the core of what we build here to keep predictive maintenance models running on factory floors without a dedicated...
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You'll be the third release engineer on a team of twelve, working directly with the platform and applied ML teams to ship model updates safely at scal...
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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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