软件开发工程师，质量平台与AI测试自动化

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.

- Build test platforms, automation frameworks, validation services, and AI-powered tools to improve testing efficiency and release quality.

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.

- 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.

- Experience building reusable testing infrastructure, automation platforms, AI tools, or internal productivity tools used by multiple teams.

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.