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.
As a fully async team spread across 9 time zones, we believe great engineers can build reliable systems without ever sharing an office. Our team ships...
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We just closed our Series A and are scaling fast — customer model traffic has grown 6x in the last two quarters and our single-region deployment setup...
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We just closed our Series B and engineering teams using our platform are shipping faster than ever — which means riskier pull requests are slipping th...
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PyTorch model registries, Kubernetes deployments, and Airflow-orchestrated rollout pipelines are the core of what our platform manages for customers....
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The models you'll help serve process millions of inference requests a day, and right now a meaningful share of our p99 latency comes from cold starts...
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You'll join an edge platform squad of seven, working directly with our firmware and on-device model teams on a growing operational problem: a fleet of...
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We don't care where you work from. We care about output, clear communication, and ownership of the small things that make a deployment pipeline trustw...
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We just closed our Series B and are scaling our observability platform fast. We need a senior engineer who can build the chaos simulation layer our la...
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You'll be the third platform engineer on a team of nine, working directly with our infrastructure lead on a problem every ML platform eventually hits:...
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Posted 2 months ago
United States — Los Angeles (UTC-7)
GPU hours, priority queues, and a research team that always needs more compute than we have are the core of what this role manages. If you've built a...
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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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