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Position: Machine Learning Engineer, TikTok BRIC
Institution: TikTok
Location: Singapore
Duties: 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; Uplevel risk machine learning excellence on privacy/compliance, interpretability, risk perception and analysis
Requirements: Master or above degree 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, transfer/multi-task learning, reinforcement learning, time series or graph unsupervised learning
   
Text: Machine Learning Engineer, TikTok BRIC 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; Uplevel risk machine learning excellence on privacy/compliance, interpretability, risk perception and analysis Master or above degree 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, transfer/multi-task learning, reinforcement learning, time series or graph unsupervised learning
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