Among computer and information systems managers, AI is already used for some of the tasks. Measured exposure for computer and information systems managers: 16% (some).

Share of this job’s tasks that AI is already used for at work, from real usage. It is not a forecast of job loss, and a low number means no use seen yet. Source: Anthropic Economic Index.

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About the job

At d-Matrix , we are focused on unleashing the potential of generative AI to power the transformation of technology. We are at the forefront of software and hardware innovation, pushing the boundaries of what is possible. Our culture is one of respect and collaboration.

We value humility and believe in direct communication. Our team is inclusive , and our differing perspectives allow for better solutions. We are seeking individuals passionate about tackling challenges and are driven by execution. Ready to come find your playground? Together , we can help shape the endless possibilities of AI.

Director, Software Quality Engineering

Location: Santa Clara, CA (Hybrid on-site 3 days a week)

Reports to: Vice President of Software

Employment type: Full-time, exempt

About the Role

The Director, Software Quality Engineering leads quality across our firmware, compiler, runtime, and inference-serving software. You will set the vision, define the test architecture, and lead both automation strategy and day-to-day execution for the Software Quality Engineering team.

Your first priority will be to assess our current processes, test content, tools, and frameworks. Create the practical roadmap while keeping current qualification work on schedule and developing the engineers who own it.

Automation and AI-assisted engineering are central to this role. Build on the foundation and extend the team’s capacity to meet growing product and release complexity through automation, reusable tooling, and AI-assisted practices. You will stay hands-on enough to review framework code, investigate complex failures, and guide technical decisions.

Our work spans numerical accuracy, performance, and stability on evolving hardware and software. Because test capacity is finite, you will make coverage decisions and help the team use evidence to distinguish product defects, infrastructure problems, and unreliable tests.

You will also lead the company's open-source and proprietary software license compliance program (inbound and outbound) and coordinate the company's AI use policy, working closely with Legal and other stakeholders.

Key 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, with deliberate choices about 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 turn lessons from customer-reported defects into better 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

Scope Clarification

This role does not include:

Release assembly and maintenance

Building a large manual testing organization or outsourcing testing to vendors as the core strategy

Post-silicon hardware validation or silicon bring-up

Writing unit tests on behalf of development teams

First-Year Objectives (proposed)

Establish a baseline of human effort in test preparation, execution, triage, and maintenance. 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, including human review and maintenance costs.

Put documented numerical-tolerance policies and performance-regression gates into use with engineering owners so that agreed violations block progression and authorized exceptions are recorded

Deliver an actionable quality roadmap, clear operating ownership, and shared knowledge so that critical workflows do not depend on any one person.

Specific targets and measurement windows will be agreed after the baseline is established

Minimum Qualifications

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

Preferred Qualifications

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

Equal Opportunity Employment Policy

d-Matrix is proud to be an equal opportunity workplace and affirmative action employer. We’re committed to fostering an inclusive environment where everyone feels welcomed and empowered to do their best work. We hire the best talent for our teams, regardless of race, religion, color, age, disability, sex, gender identity, sexual orientation, ancestry, genetic information, marital status, national origin, political affiliation, or veteran status. Our focus is on hiring teammates with humble expertise, kindness, dedication and a willingness to embrace challenges and learn together every day.

d-Matrix does not accept resumes or candidate submissions from external agencies. We appreciate the interest and effort of recruitment firms, but we kindly request that individual interested in opportunities with d-Matrix apply directly through our official channels. This approach allows us to streamline our hiring processes and maintain a consistent and fair evaluation of al applicants. Thank you for your understanding and cooperation.