Director, Software Quality Engineering
About The Position
The Director, Software Quality Engineering leads quality across d-Matrix’s firmware, compiler, runtime, and inference-serving software. The role sets the quality vision, defines test architecture, and leads automation strategy and day-to-day execution for the Software Quality Engineering team. The initial priority is to assess existing processes, test content, tools, and frameworks; establish a practical roadmap; keep current qualification work on schedule; and develop the engineers responsible for that work. Automation and AI-assisted engineering are central to the position. The Director will expand team capacity through automation, reusable tooling, and AI-assisted practices while remaining hands-on enough to review framework code, investigate complex failures, and guide technical decisions. The role addresses numerical accuracy, performance, and stability across evolving hardware and software, making evidence-based coverage decisions and helping distinguish product defects from infrastructure problems and unreliable tests. The Director also leads d-Matrix’s open-source and proprietary software license compliance program, covering inbound and outbound obligations, and coordinates the company’s AI use policy with Legal and other stakeholders. The position is based in Santa Clara, California, with a hybrid schedule requiring three on-site days per week, and reports to the Vice President of Software.
Requirements
- 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 in firmware, compilers, runtimes, drivers, distributed systems, or comparable software, including hardware/software integration with real devices.
- Ability to evaluate AI-assisted engineering workflows and judge whether AI-generated tests verify intended behavior rather than merely pass.
- Experience designing test coverage under resource constraints, integrating tests into CI, and defining promotion and release gates.
- Sound judgment about coverage, risk, and metrics, and the ability to work credibly with developers and communicate clear quality assessments.
Nice To Haves
- 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.
- Experience with hardware-in-the-loop testing, device farms, or lab automation.
- Experience managing open-source license compliance.
Responsibilities
- Define the quality strategy, technical roadmap, and test architecture across firmware, compiler, runtime, and inference serving.
- Build reusable test frameworks and automated functional, integration, and end-to-end coverage, choosing what runs on available hardware and when.
- Develop continuous soak testing to assess long-run stability and produce useful failure evidence.
- Establish numerical comparison and tolerance policies with engineering owners, and detect performance regressions against reliable baselines.
- Define measurable QA readiness criteria and quality gates; qualify the exact release candidate, including targeted customer fixes; and communicate results, gaps, and risks clearly.
- Improve defect triage and use lessons from customer-reported defects to improve coverage and engineering practices.
- Evaluate and adopt AI-assisted test generation, triage, and failure clustering where they deliver measurable benefits.
- Establish and maintain processes for open-source and proprietary software license compliance, including inbound and outbound obligations, in partnership with Legal.
- Coordinate implementation and ongoing review of the company’s AI use policy with legal, security, and engineering leadership.
- Coach and develop the software quality engineering team.
- Assess current processes, test content, tools, and frameworks; develop a practical roadmap while keeping qualification work on schedule.
- Review test framework code, investigate complex failures, and guide technical decisions.
- Establish a baseline of human effort in test preparation, execution, triage, and maintenance, then measurably reduce repetitive work on comparable workloads while preserving or improving coverage and defect detection.
- Evaluate AI-assisted testing on representative work and demonstrate gains in time to actionable results or engineering effort, accounting for human review and maintenance costs.
- Put documented numerical-tolerance policies and performance-regression gates into use with engineering owners so agreed violations block progression and authorized exceptions are recorded.
- Deliver an actionable quality roadmap, clear operating ownership, and shared knowledge so critical workflows do not depend on any one person.
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What This Job Offers
Job Type
Full-time
Career Level
Director
