Machine Learning Engineer

2 weeks ago

Singapore TIKTOK PTE. LTD. Full time

Responsibilities Team Introduction: TikTok LIVE ecosystem team is dedicated to creating a pleasant and healthy ecosystem environment for all users and creators, pursuing the mission of LIVE "to inspire interactive creations. Here, you can explore cutting-edge recommendation system technology, revamp the algorithm for ecosystem and business goals across the entire system, and collaborate with the most creative, imaginative, and mission-driven colleagues to continuously drive the evolution of the LIVE ecosystem. Build industry leading recommendation system; develop highly scalable classifiers and tools leveraging machine learning; Understand product objectives and machine learning techniques; improve model and recommendation strategy; Understand user behavior and apply ML algorithms to optimize content consumption and production experience; Understand content security strategy and apply ML algorithms to improve content audit process; Work with cross functional teams to grow ByteDance product in important regional markets. Qualifications Bachelor's degree in computer science or a related technical discipline, with at least 2 years of related work experience; Solid experience with data structures or algorithms；Proven problem-solving and analytical skills, with capability to tackle medium-to-high complexity algorithmic challenges; Or ability to independently design, implement, and optimize fundamental algorithms (e.g., sorting, searching, graph algorithms, dynamic programming) 2+ years of hands-on coding experience in mainstream programming languages (e.g., Python, Java, C++). Participation in at least one relevant project, with the ability to demonstrate tangible outcomes (e.g., improvement in model accuracy, system performance optimization), or have publications in relevant fields or experience in technical presentations/sharing will be considered a strong plus. Strong communication and teamwork skills; Passion about technologies and solving challenging problems. #J-18808-Ljbffr

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