Software Quality Engineer

d-Matrix

. Translate requirements, changelogs, and engineering handoffs into test plans with clear scope, configurations, expected results, and evidence .

Posted 10/19/2026full-timeBelgrade • SerbiaJuniorMid-LevelWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates strong software development and test engineering capabilities, with expertise in Python for test automation and AI-assisted engineering. Proficient in designing comprehensive test plans, executing automated tests, and collaborating effectively with cross-functional teams.

Highest-signal resume keywords

Python ProgrammingTest AutomationAI-Assisted EngineeringLinux-Based EnvironmentsSystems Software Testing

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Hard Skills

Test DesignAutomated Functional TestingIntegration TestingEnd-to-End TestingFailure InvestigationVersion ControlCode ReviewTest Framework ExtensionML InferenceNumerical Comparisons

Soft Skills

Clear Written CommunicationSound JudgmentCollaboration

Tools & Technologies

CI PipelinesContainersTest-Result ReportingLab Automation

Industry Keywords

Firmware TestingDrivers TestingCompilers TestingRuntimes TestingDistributed Systems

Tech Stack

Tools & technologies

Distributed SystemsLinuxNode.jsPythonC++

About the role

Key responsibilities & impact

Translate requirements, changelogs, and engineering handoffs into test plans with clear scope, configurations, expected results, and evidence
Build and maintain automated functional, integration, and end-to-end tests
Integrate tests into CI, nightly, and release workflows
Run qualification against exact candidate builds and supported configurations
Record hardware, software versions, commands, inputs, and results for reproducibility
Investigate failures using code, logs, and focused experiments
Distinguish product defects from environment problems and unreliable tests
Report defects with expected and actual behavior, reproducible steps, and evidence
Verify fixes and document unresolved results and coverage gaps
Assess numerical accuracy, performance, and stability against agreed tolerances and reliable baselines
Use AI for test design, coding, and log analysis while reviewing and verifying outputs
Prioritize coverage against product risk and available hardware
Document workflows, results, and handoffs for repeatable execution
Partner with Quality Engineering, Product, and Engineering colleagues across locations

Requirements

What you’ll need

Strong software development ability and practical software quality or test engineering experience
Programming skills for test automation and tooling; Python is used in the current workflow
Experience designing tests from requirements, including negative cases, boundary conditions, and integration behavior
Experience investigating failures with logs, configuration evidence, and reproducible experiments
Experience improving workflows through automation and reusable tooling
Practical use of AI-assisted engineering for test planning, coding, or failure analysis
Ability to work in Linux-based development or test environments
Experience with version control, code review, and automated test workflows
Clear written communication and sound judgment about risk and uncertainty
Ability to collaborate with distributed teams
2+ years of experience preferred
Experience with systems software testing in firmware, drivers, compilers, runtimes, or distributed systems preferred
ML inference experience, including model execution, numerical comparisons, quantization, or performance benchmarks preferred
Experience with Python test frameworks, CI pipelines, containers, test-result reporting, or lab automation preferred
Hardware-in-the-loop testing, multi-card or multi-node configurations, and extended stability or soak testing preferred
Experience extending a test framework or introducing a human-reviewed AI workflow preferred
Experience with C++ or another systems language preferred

Benefits

Comp & perks

Inclusive and collaborative workplace culture
Equal opportunity workplace and affirmative action employer
Opportunities to work with distributed teams across locations
Practical AI-assisted engineering workflows
Software and hardware innovation environment