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d-Matrix

Delivers memory-integrated AI compute platforms

Director, Software Quality Engineering

Test Automation EngineeringConfirmed live in the last 24 hours
Full-timeSanta Clara, CA, USA$195k - $330k /yr

About the Job

Requirements

  • A 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 determine whether AI-generated tests verify intended behavior rather than merely pass.
  • Experience designing test coverage under resource constraints, integrating tests into continuous integration, 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.

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 quality assurance 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.
  • 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, 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.

Desired 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.

About the Company

d-Matrix

Delivers memory-integrated AI compute platforms

d-Matrix provides scalable AI compute platforms for large datacenters with a focus on energy efficiency. Its core offering, the digital in-memory compute (DIMC) engine, places compute directly in programmable memory and uses a fabric of low-power, modular chiplets plus software to optimize AI inference. It differentiates itself by combining memory-integrated computation with a modular, chiplet-based design that reduces power use and data movement for high-volume workloads. The goal is to help large datacenters run AI workloads more efficiently at scale through customizable, energy-efficient compute solutions.

Company size
201-500
Company stage
Series C
Total funding
$429M
Headquarters
Santa Clara, CA
Founded
2019

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