Software Test Engineer Graduate (AI) - 2027 Start
TikTok
- Location
- Singapore, Singapore, Singapore
- Employment
- Full Time
- Work model
- On-Site
- Level
- New Grad
- H-1B history
- 148 approvals (FY2023)
Skills
About this role
The TikTok Engineering Test Team is dedicated to ensuring the quality, reliability, and security of TikTok's products at global scale. We partner closely with software engineers, algorithm engineers, and product teams to accelerate feature delivery while maintaining exceptional product quality.
Our team is redefining how quality assurance is built. We leverage Large Language Models (LLMs), AI agents, and intelligent automation to reimagine the entire testing lifecycle—from test design and execution to bug analysis, quality evaluation, and engineering productivity. Our mission is to build next-generation AI-powered testing capabilities that enable faster, smarter, and more scalable software delivery. Joining our team and working at the intersection of Software Engineering, Quality Engineering, and Generative AI, helping shape the future of AI-native software testing at TikTok.
We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume. Candidates can apply to a maximum of two positions and will be considered for jobs in the order you apply. The application limit is applicable to our Company and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early.
Responsibilities - Design and develop AI-powered testing solutions that improve software quality and engineering efficiency across the entire software development lifecycle. - Work on quality and engineering efficiency initiatives leveraging LLMs, including change risk analysis, automated testing, search quality evaluation, and LLM security testing. - Identify quality and engineering efficiency pain points in collaboration with software and algorithm engineering teams, propose practical optimization solutions, and drive their implementation to improve product quality and engineering efficiency. - Stay up to date with the latest advancements in Large Language Models (LLMs) and AIGC technologies, explore innovative quality and engineering efficiency solutions for software testing, and continuously refine them through practical application.