Why it’s worth it:
ReliaQuest’s Quality Engineering team is evolving quality from a test-execution function into a platform capability focused on automation, reliability, and production safety. As an Associate Software Development Engineer in Test, you will help build automated testing and continuous integration capabilities across backend services, web applications, and mobile platforms.
This is a code-first, automation-focused engineering role—not a manual testing or documentation-only position. You will gain hands-on experience with production-level systems, test automation, CI/CD, failure analysis, and AI-assisted engineering practices. Training, pairing, and guidance will be provided, with attitude and aptitude valued over previous job titles.
The everyday hustle:
- Build and maintain automated API smoke and regression tests for backend services.
- Contribute to UI automation using Playwright and, based on interest and business needs, mobile automation.
- Improve test reliability, test data setup, and environment stability.
- Develop and maintain automated tests that run within GitLab CI pipelines.
- Investigate flaky or failing pipelines and, with guidance, determine whether failures are caused by product defects, test code, or environmental issues.
- Analyze logs, metrics, and test reports to identify and troubleshoot failures across services and environments.
- Create clear, actionable bug reports that include reproduction steps and relevant technical details.
- Collaborate with Software Engineering, SRE, and DevOps teams to resolve quality and reliability issues.
- Contribute to test standards, automation frameworks, pre-commit hooks, engineering helpers, and other quality tooling.
- Help shape how quality is consistently measured and enforced across services.
- Use Claude and other AI-assisted engineering tools to accelerate test discovery, automation design, code review, failure triage, and technical documentation.
- Identify repeatable Quality Engineering tasks where AI can improve speed, consistency, and effectiveness.
- Carefully validate and refine AI-generated outputs to ensure they meet engineering, reliability, and security expectations.
- Share useful AI prompts, patterns, and workflows with the broader QA/SDET team.
- Continuously improve automation and quality practices to help prevent incidents rather than only identify defects after they occur.
Do you have what it takes?
- Foundational experience reading and modifying code in Java, TypeScript, or a similar programming language.
- Basic knowledge of REST APIs, HTTP methods, and HTTP status codes.
- Familiarity with Git and branch-based development workflows.
- A willingness to debug issues across multiple layers, including test code, services, pipelines, and environments.
- An automation-first mindset and an interest in building reliable, maintainable test solutions.
- Working knowledge of Claude or similar AI-assisted engineering tools and an understanding of how they can support test design, automation, debugging, failure analysis, and productivity.
- The judgment to validate, refine, and correct AI-generated recommendations rather than accepting them without review.
- Strong analytical and problem-solving skills.
- Clear written and verbal communication skills, particularly when documenting defects and collaborating with technical teams.
- A collaborative mindset and eagerness to learn through training, pairing, and feedback.
What makes you uncommon?
Exposure to CI/CD pipelines or automated quality gates.
- Experience with test automation frameworks or API testing tools.
- Familiarity with Playwright, Selenium, or mobile automation technologies.
- Experience reviewing logs, dashboards, metrics, or monitoring tools.
- Exposure to production or staging incidents and root-cause investigation.
- Experience using AI tools to generate or improve automation scripts, test cases, API scenarios, or debugging workflows.
- Familiarity with prompt-writing techniques for technical work such as log analysis, code explanation, test coverage review, and root-cause analysis.
- An interest in improving shared automation platforms, developer tooling, and organization-wide Quality Engineering practices.
