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d-Matrix

Delivers memory-integrated AI compute platforms

Staff Quality Automation Engineer

Test Automation EngineeringConfirmed live in the last 24 hours
Full-timeBelgrade, SerbiaNo salary listedIn-person

About the Job

Requirements

  • At least 5 years of experience.
  • Strong software-development skills, including writing, debugging and maintaining code, explaining design choices, reviewing others’ work, and investigating failures beyond a test’s surface result.
  • Experience building shared test automation, frameworks or engineering tools that others use repeatedly.
  • Evidence of turning prototypes or manual workflows into reliable tools, including configuration, failure handling, documentation, adoption and maintenance.
  • Practical test engineering experience with requirements, boundary and negative cases, coverage, and failure investigation using code, logs and reproducible experiments.
  • Experience with Linux, version control, code review, automated tests and continuous integration workflows.
  • Regular use of AI coding agents or AI workflows, with examples of what was built, how the approach evolved and how results were checked.
  • Experience guiding technical work across distributed teams through design and code reviews; ability to explain tradeoffs, understand users’ needs, make progress independently and raise missing decisions promptly.

Responsibilities

  • Work with Quality Engineering and Engineering to understand users’ needs, agree priorities, define scope and expected results, raise dependencies and blockers, and seek help reaching the right owner.
  • Build and maintain shared automation, test frameworks and harnesses for functional, integration, regression and release testing across supported software and hardware configurations.
  • Turn scripts and prototypes into usable tools with clear interfaces, configuration, setup instructions, error handling and diagnostics; review code and test the tools.
  • Integrate tools with existing continuous integration, nightly and release workflows alongside infrastructure owners; record builds, configurations, commands, inputs and results so another engineer can reproduce failures.
  • Support adoption through documentation, examples and user feedback; check that colleagues can use tools independently; measure human effort, reliability and result quality, including review and maintenance costs.
  • Assess coverage, assertions, expected results and negative cases; distinguish product deficits, environment problems and unreliable tests; work with developers to verify fixes and provide testing cover while progressing shared automation.
  • Keep up with advances in AI coding agents and workflow tools, test promising approaches on real Quality Engineering work, share findings and build on approaches that improve results; use approved tools, check outputs and follow approved data-handling rules.
  • Understand one Quality Engineering workflow, its users, dependencies and failure modes, and establish an improvement baseline during the initial 90 days.
  • Deliver a reviewed automation improvement for an agreed need in the existing test environment.
  • Demonstrate reliable repeat execution, useful diagnostics and reproducible results against identified builds and configurations.
  • Enable intended users to set up, run and troubleshoot the tool from its documentation, and act on their feedback.
  • Compare effort, reliability or result quality with the baseline and hand over operating and maintenance notes.
  • Agree scope, targets and measurement windows with the manager after onboarding.

Desired Qualifications

  • Systems software testing in firmware, drivers, compilers, runtimes or distributed systems, including real devices.
  • Machine learning inference, model execution, numerical comparisons, quantization or performance benchmarks.
  • Test frameworks, continuous integration pipelines, containers, reporting or lab automation.
  • Hardware-in-the-loop, multi-card or multi-node configurations, and extended stability or soak testing.
  • Internal engineering products used across teams, or human-reviewed AI workflows used repeatedly.

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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