Drimva

d-Matrix

Director, Software Quality Engineering

$195K–$330K

Santa Clara · Hybrid · Director · Full-time · Posted Oct 15, 2026

Experience
No years specified.
Education
Bachelor's degree in computer science, engineering, or a related field, or equivalent practical experience.

About the job

Leads software quality strategy and execution across firmware, compiler, runtime, and inference-serving software, including test automation, release qualification, AI-assisted engineering, and software license and AI policy compliance.

What you'll do

  • Set quality strategy, test architecture, automation, coverage, and release-readiness criteria; qualify release candidates and communicate risks.
  • Develop the software quality engineering team and improve testing, defect triage, numerical accuracy, performance, and stability practices.
  • Lead software license compliance and coordinate the company’s AI use policy with Legal and other stakeholders.

What they ask for

  • Bachelor's degree in computer science, engineering, or a related field, or equivalent practical experience.
  • Experience setting quality strategy and test architecture while leading daily delivery and developing a software quality or test engineering team.
  • Strong programming ability in Python and/or C++, including building test frameworks and reviewing test code.
  • Systems software testing experience, including hardware/software integration with real devices; experience with CI, coverage under resource constraints, and promotion or release gates.

Nice to have

  • Knowledge of machine learning or inference, including quantization, numerics, and model accuracy evaluation; familiarity with inference-serving software and relevant performance benchmarks.
  • Experience with Kubernetes-based test environments, hardware-in-the-loop testing, device farms, lab automation, or open-source license compliance.

Pay

$195K – $330K USD base salary

Tools

Python, C++, Kubernetes

AI focus

Evaluate and adopt AI-assisted test generation, triage, and failure clustering; assess AI-assisted engineering workflows and coordinate the company's AI use policy.

Model-written summary · the original posting is authoritative

First seen Oct 16, 2026 · Last verified Nov 5, 2026 (UTC)