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.
Our GPU clusters handle 2.3 billion inference requests per month, and that number doubles every quarter. The bottleneck isn't compute — it's orchestra...
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Our clients send 5 million LLM API calls per day, and 40% of them share identical system prompts. Without caching, every call reprocesses the same 2,0...
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GPU spot instances cost 70% less than on-demand — but they can be reclaimed with 2 minutes notice. For ML training jobs that run for hours, a sudden i...
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Model updates should be seamless — users shouldn't notice the transition from v2.3 to v2.4. Blue-green deployments make this possible by running both...
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When an AI system makes a decision that affects someone's insurance premium, loan application, or medical treatment, regulators want to know exactly h...
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Hospitals want to train models on patient data without sharing it across institutions. Federated learning makes this possible — each hospital trains l...
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PyTorch, Airflow, and Kubernetes are the core of what we build. If you've ever been the person who gets paged when a model serving endpoint goes down...
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Our clients include Fortune 500 companies in financial services, and they come to us with one question: how do we adopt AI without breaking compliance...
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We have 12 ML models that work beautifully in notebooks. Getting them into production is another story. Our data science team is brilliant at modeling...
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Our ML platform serves 8 internal teams and supports over 40 models in production. It's built on GCP with Kubernetes, MLflow, and Airflow — and it wor...
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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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