Machine Learning Engineer , BRIC (Singapore) – 2025 Start

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Machine Learning Engineer, BRIC (Singapore) – 2025 Start

Company Overview
A global leader in short-form video content with a mission to inspire creativity and bring joy. With headquarters in Singapore and Los Angeles and offices in major cities worldwide, it focuses on empowering imagination through innovative and secure digital experiences. This company’s Business Risk Integrated Control (BRIC) team is dedicated to protecting users, ensuring platform integrity, and upholding the authenticity of the community experience.

Role Summary
As a Machine Learning Engineer in the BRIC team, you will contribute to high-stakes security initiatives that safeguard user experience. Your role will involve developing scalable machine learning solutions to mitigate platform risks, such as fake accounts, fraudulent activity, and other inauthentic behaviors. You’ll work on advancing risk management models and algorithms, enhancing platform resilience, and ensuring privacy-safe, user-friendly solutions.

Key Responsibilities

• Develop machine learning models to mitigate business risks like abusive accounts, fake engagements, and fraud.
• Enhance modeling infrastructures, automation, and robustness of algorithms to address evolving risks.
• Elevate machine learning standards with a focus on privacy compliance, interpretability, and data-driven risk analysis.

Qualifications

• Minimum Requirements:
  • Bachelor’s degree or higher in computer science, statistics, or a machine-learning-intensive field.
  • Proficient engineering skills in at least two of the following: Linux, Hadoop, Hive, Spark, or Storm.
  • Strong foundation in machine learning, with proficiency or publications in modern ML theories and applications such as neural networks, reinforcement learning, or time series.
• Preferred Qualifications:
  • Research experience in applied machine learning, ML infrastructure, recommendation systems, or large-scale ML products.
  • Strong record of publications in leading conferences or journals (e.g., NeurIPS, ICML, ICLR, CVPR).

Disclaimer:
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