Remote positions

Remote Kubernetes Jobs

Browse vetted remote Kubernetes positions — AI infrastructure, MLOps platforms, GPU cluster orchestration, and model serving at scale.

Vetted companies
Remote worldwide
All job types
MLOps Platform Engineer Kubernetes AI Infrastructure Engineer GPU Cluster Engineer KServe Deployment Engineer Cloud-Native AI Infrastructure Engineer

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Jobs & Projects

Hex
Full-time
English
Open job
Fireworks AI
staff
Full-time
English
Open job
Supabase
Full-time
English
docker aws ci/cd kubernetes vector databases +1 more
Open job
Fireworks AI
staff
Full-time
English
machine learning python pytorch generative ai machine learning +7 more
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Snowflake
senior
Full-time
English
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Supabase
Full-time
English
aws kubernetes vector databases supabase
Open job
ClickHouse
senior
Full-time
English
Open job
Fireworks AI
staff
Full-time
English
Open job
Snowflake
senior
Full-time
English
Open job
ClickHouse
Full-time
English
aws azure gcp kubernetes distributed systems +1 more
Open job

Role guide

What to expect in remote Kubernetes jobs

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 you'll work on

  • Design Kubernetes clusters for distributed model training with GPU nodes
  • Deploy AI models with autoscaling using KServe or Seldon
  • Build the MLOps platform your data science team runs experiments on
  • Manage resource quotas and scheduling for multi-team AI workloads

What companies look for

  • GPU node pool configuration and scheduling experience
  • Knowledge of Kubeflow, KServe, or Seldon for ML serving
  • Helm chart development and GitOps deployment practices

Frequently asked questions

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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