QE Architect

Luminary CloudSan Mateo, CA

About The Position

Luminary is seeking an AI-First QE Architect to lead quality engineering for their entire platform. This senior individual contributor role involves defining how quality is integrated into the system from the start, with a focus on AI. The role covers data generation, physics AI model training and inference, the supporting platform, and the developer-facing SDKs and UIs. The QE Architect will collaborate with platform engineering, ML, and product teams to ensure Luminary's Physics AI systems are reliable, accurate, and production-ready for demanding environments.

Requirements

  • Bachelor’s degree or higher in Computer Science, Data Science, Statistics, Applied Mathematics, or related fields.
  • 5+ years of industry experience as a QE architect for an entire platform stack.
  • Proficiency in Python, with experience writing production-level code.
  • Familiarity with BASH and SQL.
  • Solid experience with at least one cloud platform such as Google Cloud Platform (GCP).
  • Experience with Docker and Kubernetes.
  • Experience with QE frameworks for performance testing, scale testing, and longevity testing.
  • Comfortable with building software using Claude Code.
  • Demonstrated experience as a QE architect.
  • Ability to work as an IC across teams influencing developers.
  • Hands-on and writing code with velocity.

Nice To Haves

  • Strong programming skills in additional languages such as Go and C++.
  • Experience with Vertex AI.
  • Heavy interest in physics, with some exposure a plus.

Responsibilities

  • Own quality engineering across the entire Luminary platform.
  • Define how quality is built into the system from the ground up, with AI at the center of the approach.
  • Ensure that Luminary's Physics AI systems are reliable, accurate, and production-ready.
  • Build and maintain the QE framework, test automation, and integrate them into CI/CD and regression test pipelines.
  • Influence developers to adopt frameworks and improve test coverage.
  • Write production-level code with velocity.
  • Operationalize a system of agents into the code review, test development, and test execution process, creating an automated factory.
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