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Cancún, Quintana Roo, México
Remote only
TrueML Products is seeking an early-career Quality Engineer I to join our Quality Engineering team and help ensure the delivery of high-quality software products. This is a hands-on role focused on executing tests, finding and documenting issues, and learning best practices in software quality.
You’ll work closely with more senior Quality Engineers, developers, and product managers to validate new features, support regression efforts, and continuously improve how we test. This is an ideal role if you’re early in your QA/QE career, eager to learn, and curious about using GenAI tools to assist with testing.
Execute test plans and test cases as directed by senior QE team members.
Perform basic functional, regression, and integration testing across Web, API, and/or Data components as applicable.
Identify, document, and track defects accurately in our bug tracking system (e.g., Jira), including clear reproduction steps and impact.
Contribute to the creation and maintenance of test data needed for test execution.
Assist in troubleshooting and investigating issues by reproducing defects, gathering logs, and validating fixes.
Contribute to basic test scripts (manual and automated) under guidance, following established patterns and standards.
Participate in team meetings, providing clear status updates on assigned tasks.
Communicate testing progress, risks, and issues to your team in a clear and concise way, including to non-technical stakeholders when needed.
Ask questions and seek clarification proactively to fully understand requirements, scope, and expected behavior.
Follow through on commitments, take responsibility for your work, and deliver assigned tasks on time with close supervision.
Collaborate with your team to unblock issues within the scope of your work, escalating when needed.
Learn and adhere to established QE standards, processes, and best practices (including test case structure, defect reporting, and regression workflows).
Build a basic understanding of the software development lifecycle (SDLC) and how quality is embedded throughout.
Begin working within Agile/Scrum practices (e.g., participating in sprint ceremonies, using user stories, understanding acceptance criteria).
Assisted Scripting: Use AI-powered code completion tools (e.g., GitHub Copilot) to help write simple test automation scripts or snippets under supervision.
AI-Suggested Test Cases: Leverage AI tools to generate basic test case ideas and scenarios from requirements or user stories, then refine them with guidance.
Intelligent Search: Use AI-enhanced search within bug tracking and documentation systems to quickly find duplicate issues, related tickets, or supporting documentation.
Build awareness of how to find information and who to ask across the company (e.g., “Who can answer X?” or “Where is Y documented?”).
Degree in Computer Science, Engineering, Information Systems, or a related field.
1–2 years of relevant experience in software testing, quality assurance, or a related technical role (including internships, co-ops, or academic projects).
Basic understanding of software development lifecycles and testing methodologies (e.g., functional, regression, integration testing).
Familiarity with basic testing concepts and terminology (e.g., test cases, test data, defects, severity/priority).
Basic proficiency with operating systems and common software applications.
Exposure to bug tracking systems (e.g., Jira).
Academic or practical exposure to scripting/programming languages (e.g., Python, JavaScript, TypeScript, Java) for basic test scripting or automation.
Familiarity with Agile methodologies (e.g., Scrum or Kanban) and working with user stories.
Experience with any test management tools (e.g., Xray, TestRail) is a plus.
Experience using AI-powered tools in an academic, personal, or professional context (e.g., for code completion, documentation search, or generating test ideas).
Strong verbal and written communication skills, with the ability to clearly describe issues and test results.
Demonstrated ability to follow instructions, learn new tools and processes, and complete assigned tasks with supervision.
Interest in and willingness to use GenAI tools (e.g., GitHub Copilot, Gemini, AI test generators) to assist with scripting, test design, and information discovery.
