Software Engineer (SE / Sr SE), Data & ML Platform

Location: Santa Clara, CA; hybrid workplace

Company

hirify.global operates a compute platform for large-scale data processing, simulation, auto-labeling, scenario mining, and model training.

What you will do

• Operate and evolve production Kubernetes clusters, including bare-metal provisioning, highly available control planes, node lifecycle, GPU container runtime, networking, and storage.
• Build GitOps-based delivery for platform services and user applications with Argo CD, Helm, and Kustomize.
• Develop multi-tenant capabilities for scheduling, resource isolation, storage, networking, access control, secrets, and observability.
• Improve CPU/GPU utilization and cost efficiency across the compute platform.
• Build reusable distributed batch and workflow platforms for Spark processing and GPU-based replay and simulation.
• Follow Quality Management System requirements and contribute to continuous improvement.

Requirements

• BS, MS, or PhD in Computer Science or a related technical field, or equivalent practical experience.
• Hands-on experience operating production Kubernetes clusters, including node lifecycle management, upgrades, and troubleshooting.
• Experience with GitOps and infrastructure as code.
• Experience with GPU or ML workload scheduling, queueing and priorities, fractional GPU sharing, autoscaling, or multi-tenant resource management.
• Strong ownership, self-direction, curiosity, and ability to drive projects end to end.
• Level is determined by experience, technical depth, scope of ownership, and demonstrated impact.

Nice to have

• Experience with Ray or Kubeflow.
• Experience with Delta Lake or Apache Iceberg.
• Experience operating large-scale distributed data-processing and workflow systems.
• Hands-on experience with Apache Spark and working knowledge of Argo Workflows or an equivalent orchestrator.

Culture & Benefits

• Full-time employment in a hybrid workplace.
• Strong fundamentals, ownership, and the ability to learn are valued over experience with every technology in the stack.