Platform Engineer: Kubernetes & ML Data Pipelines

PlusAI, Inc.; Santa Clara (CA); Hybrid; USD 160,000 - 210,000; Full time

Job summary

PlusAI, Inc. is seeking a Software Engineer (SE) or Senior SE to own data and ML platform workloads on a Kubernetes-backed infrastructure.

You will optimize reliability, efficiency, and self-serve onboarding for large-scale processing, batch workflows, and GPU-accelerated tasks. You will work across scheduling, resource isolation, storage, and observability, building reusable multi-tenant capabilities and end-to-end pipelines with Argo/ Spark integrations.

Qualifications

• BS, MS or PhD in Computer Science or related field, or equivalent practical experience.
• Hands-on experience operating production Kubernetes clusters — node lifecycle, upgrades, troubleshooting.
• Experience with GPU or ML workload scheduling, queueing and priorities, fractional GPU sharing, autoscaling, or multi-tenant resource management.
• Self-driven with a strong sense of ownership: eager to take responsibility and drive projects forward end to end.

Responsibilities

• Operate and evolve production Kubernetes clusters end to end.
• Build safe, repeatable GitOps-based delivery for platform services and applications.
• Develop multi-tenant platform capabilities for scheduling, isolation, storage, networking, and observability.
• Build reusable distributed batch/workflow platforms for Spark data processing and GPU-based replay and simulation.
• Ensure work aligns with the company’s QMS requirements and drive continuous improvement.

Skills

Kubernetes

GitOps

Infra-as-code

GPU scheduling

Multi-tenant compute

Ownership

Education

BS/MS/PhD in CS

Tools

Argo CD

Helm

Kustomize

Argo Workflows

Ray

Kubeflow

Delta Lake

Apache Iceberg

Apache Spark

Job description

PlusAI, Inc. is seeking a Software Engineer (SE) or Senior SE to own data and ML platform workloads on a Kubernetes-backed infrastructure.

You will optimize reliability, efficiency, and self-serve onboarding for large-scale processing, batch workflows, and GPU-accelerated tasks. You will work across scheduling, resource isolation, storage, and observability, building reusable multi-tenant capabilities and end-to-end pipelines with Argo/ Spark integrations.