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PlusAI

Robot type
Autonomous Vehicle
Location
Santa ClaraCaliforniaUSA
Job type
Artificial Intelligence
Posted
Jul 31, 2026
Salary
$135,000–$200,000 a year
Full-time

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

Job description

All key offline workloads — large-scale data processing, simulation, auto-labeling, scenario mining, and model training — run on the compute platform this role owns. In this role, you will improve the reliability and efficiency of our Kubernetes infrastructure, make workload onboarding simpler and more self-service, and build reusable batch and workflow capabilities for petabyte-scale processing. We are looking for strong Kubernetes and platform-engineering fundamentals, depth in at least one adjacent area—distributed data processing, ML/GPU infrastructure, or multi-tenant compute systems—and the curiosity and ownership to grow across the others.

We are open to candidates at either the…

Job responsibilities

  • Operate and evolve our production Kubernetes clusters end to end: bare-metal provisioning automation, highly available control planes, node lifecycle, GPU container runtime, networking, and storage
  • Build safe, repeatable GitOps-based delivery for platform services and user applications using tools such as Argo CD, Helm, and Kustomize
  • Develop shared multi-tenant platform capabilities for scheduling, resource isolation, storage, networking, access control, secrets, and observability while improving CPU/GPU utilization and cost efficiency
  • Build and improve reusable distributed batch and workflow platforms for Spark data processing and GPU-based replay and simulation
  • Ensure that your work is performed in accordance with the company’s Quality Management System (QMS) requirements and contribute to continuous improvement efforts

Job requirements

  • BS, MS, or PhD in Computer Science or a related technical field, or equivalent practical experience
  • Hands-on experience operating production Kubernetes clusters — node lifecycle, upgrades, troubleshooting — plus GitOps and infrastructure-as-code experience
  • 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: a quick learner who is eager to take responsibility and drive projects forward end to end
  • Experience with Ray or Kubeflow
  • Experience with lakehouse technologies such as Delta Lake or Apache Iceberg
  • Experience operating large-scale distributed data-processing and workflow systems, with hands-on depth in a system such as Apache Spark and working knowledge of Argo Workflows or an equivalent orchestrator
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