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're building the operational backbone that lets our clinical research teams move faster without babysitting spreadsheets and manual handoffs. Fast-m...
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We just crossed a major usage milestone and our model-serving infrastructure is starting to show cracks under the load. We need a senior MLOps enginee...
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PyTorch, Kubernetes, and a fleet of inference endpoints serving billions of requests a month are the core of what we build. If you've ever obsessed ov...
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Posted 1 month ago
United States — Los Angeles (UTC-7)
We just closed a new funding round earmarked specifically for cutting our customers' cloud waste, and container overprovisioning is the single biggest...
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We just closed our Series B and GPU spend is growing faster than our forecasting can keep up with. We need a senior-minded analyst, regardless of titl...
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Our model snapshot tests take longer to run than the training jobs they're supposed to catch bugs in. We're looking for someone who can rebuild the sn...
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As a fully async team spread across nine time zones, we believe great engineers don't need constant check-ins to do great work. Our model registry has...
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LangChain, custom retrievers, and a growing vector index are the backbone of our RAG stack. We need someone who can benchmark retrieval latency across...
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GPU cluster provisioning at our scale still involves a platform engineer manually approving capacity requests through a Slack thread. That workflow br...
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Our inference fleet spans three cloud providers, and switching a model between them still takes an engineer half a day of manual configuration work. T...
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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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