The e-commerce industry has seen tremendous growth in recent years and has become a hotly contested space amongst leading internet companies. With millions of loyal users globally, we believe TikTok is an ideal platform to deliver a brand new and better e-commerce experience to our users. Our quality assurance engineering team is responsible for keeping an e-commerce ecosystem stable, secure, and intuitive for our users. We are looking for passionate and talented people to join us as we drive the future of e-commerce here at TikTok.
What you'll do:
Build test platforms, automation frameworks, validation services, and AI-powered tools to improve testing efficiency and release quality.
Develop AI skills/agents for QA/RD scenarios such as requirement analysis, test case generation, test data generation, log analysis, defect triage, root cause analysis, regression selection, and test reporting.
Build self-service capabilities that help QA/RD teams execute validation, monitor test results, assess release readiness, and reduce manual testing effort.
Integrate automation and AI outputs with test management systems, dashboards, ticketing systems, and release processes.
Partner with QA and RD teams to identify testing pain points and convert them into reusable automation platforms or AI-based solutions.
Work closely with global QA, RD, product, and business teams across multiple countries/regions to support international e-commerce scenarios, localization needs, and cross-market release quality. Minimum Qualifications:
Bachelor degree or above in Computer Science, Software Engineering, or a related technical field.
3+ years of hands-on software development experience, preferably in test platform, automation, AI tooling, developer productivity, or quality engineering related areas.
Experience building test automation frameworks, validation tools, internal productivity tools, or CI/CD-related engineering solutions.
Strong understanding of software testing concepts, including requirement analysis, test design, regression testing, defect analysis, and release validation.
Strong engineering sense in building reliable, maintainable, and scalable solutions rather than one-off scripts.
Ability to work closely with QA and RD teams, understand real testing pain points, and turn them into practical engineering solutions.
Able to collaborate with cross-functional teams in an international environment.
Preferred Qualifications:
Experience building reusable testing infrastructure, automation platforms, AI tools, or internal productivity tools used by multiple teams.
Experience applying AI/LLM capabilities to improve testing efficiency, such as test case generation, test data generation, log analysis, defect triage, root cause analysis, regression selection, risk assessment, or test reporting.
Experience working on complex systems where quality depends on cross-service validation, data consistency, configuration correctness, or end-to-end business flows.
Experience integrating automation or AI outputs with test management systems, dashboards, ticketing systems, or release processes.