Remote positions
Browse vetted remote Kubernetes positions — AI infrastructure, MLOps platforms, GPU cluster orchestration, and model serving at scale.
Top matching AI roles
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Full-time roles and fixed-scope freelance missions — matched to your AI skills.
Autoscaling policies for GPU inference clusters are typically reactive: scale up when utilization exceeds a threshold, scale down when it drops below...
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GPU inference throughput is a function of batch size: a GPU executing a batch of 32 inference requests is far more efficient than the same GPU process...
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Posted 3 months ago
United States — Los Angeles (UTC-7)
Have you ever debugged a distributed training job that failed at step 47,832 of 50,000 and lost 40 hours of gradient accumulation? We build the infras...
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Posted 3 months ago
United States — Los Angeles (UTC-7)
You'll be the fourth engineer on a 12-person platform team that ships MLOps infrastructure to 300+ enterprise ML teams. Our clients run thousands of m...
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Posted 3 months ago
United States — Los Angeles (UTC-7)
Training a large model for 14 days and losing everything to a hardware fault on day 13 is not a theoretical scenario — it happens regularly on GPU clu...
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Posted 3 months ago
United States — Los Angeles (UTC-7)
Federated learning on edge devices is straightforward in research settings. In production, it's a distributed systems problem: 50,000 devices connecti...
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Posted 3 months ago
United States — Los Angeles (UTC-7)
GPU vendors publish benchmark numbers. Reality disagrees. A model that achieves 1,200 tokens per second on a pristine A100 in a controlled benchmark m...
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A model doesn't deploy in isolation. It depends on feature pipelines, upstream data sources, downstream consumers, and shared infrastructure component...
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Alert fatigue is an observability epidemic. A service experiencing a single network partition can generate 800 alerts across 40 different monitors in...
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Traffic shadowing is how you validate a new model against production traffic before it touches users: mirror live requests to the candidate model, rec...
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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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