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.
Kubernetes makes it easy to run workloads. It makes it hard to know what each workload actually costs. Shared nodes, bursty resource usage, and the ga...
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Deploying a new model version is rarely just about the model. It touches the inference service, the feature pipeline that feeds it, the downstream ser...
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Posted 4 months ago
United States — Chicago (UTC-5)
A data scientist runs an experiment in January, gets a great result, and moves on to the next project. Six months later, a colleague tries to reproduc...
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Sharing GPU clusters across dozens of teams sounds simple until a rogue training job claims 80% of capacity and blocks everyone else's experiments for...
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When a serverless ML function takes 800ms instead of 200ms, is it a cold start? A memory pressure issue? A network round-trip to a bloated dependency...
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Posted 4 months ago
United States — Los Angeles (UTC-7)
Cloud compute is the biggest controllable cost for ML-heavy companies, and most of it is wasted on over-provisioned clusters, idle GPUs, and workloads...
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You can't know how a new model will behave in production until it's in production — unless you have a shadow testing framework. Shadow mode runs candi...
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Model deployments fail in ways that aren't obvious. A model ships to 10% of traffic, looks fine on standard metrics, but two hours later a specific us...
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We power the AI assistant behind 3 major airline customer support lines. When a passenger texts about a delayed flight, rebooking options, or a lost b...
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Vector databases cache embeddings to avoid recomputing them on every query. The problem is freshness: when source documents change, their cached embed...
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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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