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.
Before deploying a new model version, engineers should know: if this deployment fails, what breaks? Which downstream services depend on this model's o...
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A GPU cluster shared across 30 data science teams is a recipe for conflict. The PhD researcher running a 3-day training job blocks the engineer who ju...
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Deploying a model to the cloud takes minutes. Deploying a model to 15 million edge devices — wearables, industrial sensors, smart cameras — takes week...
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Serverless ML inference saves money when traffic is bursty — you only pay for what you use. But cold starts kill the user experience: the first reques...
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Our clients' models work. They're just too expensive to run. A single inference call costs $0.12 when it should cost $0.003. Latency is 800ms when the...
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Thirteen models in production. Four different deployment patterns. Zero standardization. That's where we are today, and that's exactly the problem you...
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Posted 4 months ago
United States — Los Angeles (UTC-7)
Rolling back a model version sounds simple until you realize the downstream impact. A recommendation model rollback might invalidate cached recommenda...
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Static autoscaling rules waste GPU capacity. Scale up too aggressively and you pay for idle GPUs during traffic dips. Scale up too slowly and requests...
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GDPR Article 44 prohibits transferring personal data outside the EEA without adequate safeguards. But in a cloud-native AI system, data crosses border...
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QA engineers spend 2 hours per day setting up test environments — provisioning databases, loading fixtures, configuring services, and waiting for ever...
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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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