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 mission is to make GPU inference predictable enough that enterprise clients can put SLAs on it. This role owns the monitoring layer that watches e...
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The pricing model you'll ship will directly influence millions of dollars in compute spend decisions made by our enterprise clients every month. Our c...
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You'll be the fourth engineer on a team of nine, and we don't care where you work from as long as the work gets done and communication stays clear. Ou...
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Posted 2 months ago
United States — Los Angeles (UTC-7)
Our fleet of edge devices ships firmware updates every week, and right now we have no reliable way to prove a device hasn't been tampered with before...
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We're building the next generation of AI-powered edge intelligence for industrial hardware and need a senior engineer to own our core modeling work. F...
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Kubernetes, Airflow, and Docker are the core of what we build. If you enjoy making other engineers' models actually run reliably in production, this r...
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AWS, Azure, and Kubernetes are the foundation of what our platform runs on. What we have already: a payments infrastructure processing millions of tra...
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Have you ever wanted to build the internal platform that other ML engineers actually enjoy using? Our clients include finance teams at large enterpris...
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Kubernetes, Docker, and multi-cloud deployment are the backbone of what our personalization team ships every day. We believe recommendation systems sh...
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We serve inference workloads for over 300 model-serving customers who expect five-nines reliability from our GPU fleet. You'll join a cross-functional...
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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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