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 the inference backbone for 14 mid-market SaaS companies. They build the apps — we keep their models serving, scaling, and not falling over at 3...
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Translating a document is easy. Translating a live customer support conversation in under 200ms while preserving tone, technical terms, and brand voic...
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Every AI model our clients deploy goes through our API gateway — 890 million requests per month across 200+ models. The gateway handles authentication...
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Static alert thresholds are the bane of every on-call engineer's existence. Set them too tight and you get paged at 3 AM for normal traffic fluctuatio...
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GDPR says European data stays in Europe. China's PIPL says Chinese data stays in China. India's DPDPA has its own rules. When you're running ML models...
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Posted 4 months ago
United States — Los Angeles (UTC-7)
GPU clusters are expensive. Idle GPUs are money on fire. Our workload scheduler decides which training jobs run on which GPUs, when to preempt low-pri...
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We run 340 ML models in production for 28 clients, and every one of those models needs to stay online, fast, and accurate. When a model goes down at 2...
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Shipping ML models to production should be as boring as deploying a web app. At most companies, it isn't — releases take days, rollbacks are manual, a...
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Our ML engineers ship great models. Getting those models into production used to take 3 weeks and a prayer. We've cut it to 5 days, but we want same-d...
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We just launched our internal ML platform six months ago, and adoption is growing faster than we expected. Thirty data scientists across three product...
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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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