Why this role stands out
Lead impactful software quality engineering at a cutting-edge AI company, shaping the future of generative AI through strategic vision and automation. This hybrid role offers a collaborative and inclusive environment where your leadership in quality will directly contribute to groundbreaking innovation, making it an ideal opportunity for experienced engineers passionate about driving excellence in a dynamic tech landscape.
Quick overview
Job description
Join d-Matrix and help build the future of AI inference. We’ve pioneered a memory-centric architecture that brings compute directly into memory, making AI faster, more energy efficient, and accessible on a global scale. From chips and software to rack-scale systems, our team is tackling some of AI infrastructure’s hardest challenges — and shipping breakthrough technology today. If you want to create what comes next and help make infinite inference possible, we’d love to hear from you.
Our culture is built on respect, collaboration, and direct communication. We value humility, we believe our different perspectives lead to better solutions, and we’re looking for people who are driven by execution and energized by hard problems.
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 individuals 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 all applicants. Thank you for your understanding and cooperation.
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