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.
Semiconductor fabs produce wafers worth $50,000 each. A single defect missed during inspection can cascade through the manufacturing process, ruining...
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We deploy ML models for 12 clients, and each deployment is its own snowflake — different frameworks, different serving requirements, different scaling...
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We serve 100 enterprise clients, and each one needs a model that understands their specific domain, terminology, and content policies. Off-the-shelf m...
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Our ML platform serves 8 internal teams — and none of them are happy with the deployment experience. Model registration takes 14 steps. Deployment con...
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We operate 200 A100 GPUs across two data centers, and utilization is our business model. Every idle GPU-hour is lost revenue. Every training job that...
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We don't care where you work from. We care about output, clear communication, and ownership. Our team of 18 is spread across 6 countries, and we've sh...
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We're building the next generation of AI-powered drug discovery tools and need a senior ML engineer who's comfortable working at the intersection of d...
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Posted 5 months ago
United States — Los Angeles (UTC-7)
The forecasting team has good ideas and not enough platform leverage. Training runs are inconsistent, feature pipelines are fragile, and deployment ha...
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We are a French public-sector technology agency operating under a mandate to modernise the digital infrastructure of government services. Our teams bu...
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We are a mining technology company based in Perth, Western Australia. We develop and deploy autonomous haulage and drill-and-blast optimisation system...
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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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