Machine Learning Engineer Graduate

5 days ago

Singapore TIKTOK PTE. LTD. Full time

Team Mission: Protect TikTok users, including and beyond content consumers, creators, advertisers. Secure platform health and authenticity of community experience. Collaborate with cross-functional stakeholders to improve TikTok infrastructures, services, tools and algorithms, towards a higher standard of privacy and security. The BRIC team works to measure and minimize the damage implied by inauthentic behaviors on TikTok and its extended platforms, covering multiple classical and novel integrity/security areas such as fake accounts, fake traffic, spam, scraping, cyberbullying, live room risks, incentive fraud, monetization abuse, etc. We are looking for talented individuals to join our team in 2026. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Launch your career where inspiration is infinite at TikTok. Successful candidates must be able to commit to an onboarding date by end of year 2026. 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 TikTok and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early. Job Responsibilities: Build machine learning solutions to respond to and mitigate business risks in ByteDance products/platforms. Such risks include and are not limited to abusive accounts, fake engagements, spammy redirection, scraping, fraud, etc. Improve modeling infrastructures, labels, features and algorithms towards robustness, automation and generalization, reduce modeling and operational load on risk adversaries and new product/risk ramping-ups. Level up risk machine learning excellence on privacy/compliance, interpretability, risk perception and analysis. Qualifications: Minimum Qualifications Final year or recent graduate with a background in computer science, statistics, or other relevant, machine-learning-heavy majors. Solid engineering skills. Proficiency in at least two of: Linux, Hadoop, Hive, Spark, Storm. Strong machine learning background. Proficiency or publications in modern machine learning theories and applications such as deep neural nets,

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