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 run over 200 training jobs a week across a GPU cluster on GKE. Utilisation sits around 40%. Jobs fail silently. Experiment results live in one engi...
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You know that 3am incident where the model silently started returning null for 8% of requests and nobody noticed for six hours because the dashboard s...
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We serve 180 million product recommendations per day across a retail marketplace. The ML systems behind those recommendations — ranking models, person...
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The numbers: 12 ML models in production, 6 of them retrained weekly, 3 teams with conflicting deployment processes, 0 shared model registry, and 1 MLO...
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50 data scientists. Every one deploys differently. Some use a shared EC2 instance. Some maintain their own containers. There is no model registry that...
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Six ML models in production. All deployed via cron jobs. No monitoring. Retraining is a manual process involving two engineers and a lot of optimism....
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Posted 5 months ago
Our ML team is scaling fast and our infra is not keeping up. We've got 12 researchers queuing jobs on a cluster that's half on-prem, half GCP, all hel...
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We are a small research lab building foundation models for scientific discovery. We need a research engineer who can implement and extend cutting-edge...
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We're moving our ML infrastructure from a setup that was fine when we had three data scientists to one that can support ten and scale to twenty. Right...
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Role summary: ML Platform Engineer, mid-level. Company: B2B SaaS, 85 employees, Series A. Team: Platform Engineering (4 engineers + 1 manager). Report...
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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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