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MLOps Engineer – Kubernetes & GPU Clusters
Amara Diallo
Full-time · Mid-level · Los_Angeles
About the role
We run over 200 training jobs a week across a GPU cluster on GKE. Utilisation sits around 40%. Jobs fail silently. Experiment results live in one engineer's personal MLflow instance. Our researchers spend more time fighting infrastructure than doing science. We need an MLOps engineer who has managed GPU workloads on Kubernetes in a real multi-team research environment — not a single training run, but sustained production load at scale.
Responsibilities
- Manage and scale our GPU Kubernetes cluster on GKE
- Build and maintain ML training pipelines with Kubeflow
- Optimise GPU utilisation and reduce idle compute costs
- Set up monitoring and alerting for training jobs
- Document infrastructure and onboard new ML team members
Requirements
- 3+ years MLOps with Kubernetes in production
- Experience managing GPU workloads (NVIDIA, CUDA, node selectors)
- Strong Python and familiarity with Helm and Kustomize
- Hands-on with Airflow or Kubeflow for pipeline orchestration
- Cloud experience (GCP preferred — GKE, Vertex AI, Cloud Storage)
Benefits
- Greenfield MLOps build at a research-heavy company
- Full remote
- Competitive salary + equity
- GPU budget for personal experiments
- 25 days PTO
Job Type
Full-time
Level
Mid-level
Language
English
Salary Range
$120,000 – $155,000
AI Expertise
MLOps & AI Infrastructure
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