Security Tools Administration: Manage the day-to-day operations, configuration, and administration of enterprise IT security tools (including firewalls, IDS/IPS, EDR, SWG, VPN, DLP, Mail Gateways, and other security-related technologies).
Performance & Health Monitoring: Proactively monitor the performance, health, and availability of security infrastructure; identify, troubleshoot, and resolve technical issues or anomalies to ensure continuous functionality.
Lifecycle & Patch Management: Perform regular maintenance activities, including updates, patches, upgrades, and configuration fine-tuning to ensure systems remain secure and up to date.
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Participate in the optimization of ByteDance LIVE business and serve the LIVE experience of billions of global users.
Adopt cutting-edge machine learning models to improve business metrics (LIVE revenue and LIVE content quality), and bring creators/operators the ultimate experience in LIVE business.
Deeply participate in product/business discussions, analyze and understand user behaviour patterns, and provide optimization directions for improvement.
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Participate in the optimization of ByteDance LIVE business and serve the LIVE experience of billions of global users.
Adopt cutting-edge machine learning models to improve business metrics (LIVE revenue and LIVE content quality), and bring creators/operators the ultimate experience in LIVE business.
Deeply participate in product/business discussions, analyze and understand user behavior patterns, and provide optimization directions for improvement.
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Participate in the optimization of ByteDance LIVE business and serve the LIVE experience of billions of global users.
Adopt cutting-edge machine learning models to improve business metrics (LIVE revenue and LIVE content quality), and bring creators/operators the ultimate experience in LIVE business.
Deeply participate in product/business discussions, analyze and understand user behaviour patterns, and provide optimization directions for improvement.
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Define and drive Service Level Objectives (SLOs) for online machine learning inference systems, ensuring the reliability, availability, and performance of large-scale production inference services;
Ensure the reliability and operational excellence of offline machine learning training pipelines, continuously improving training job success rates;
Drive infrastructure capacity planning and resource management for machine learning workloads, ensuring compute resources meet evolving business demands while continuously improving GPU and CPU utilization through performance optimization;
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Individuals who are completing or have recently completed a Bachelor’s degree in Business, Data Science, Computer Science, Information Technology, Communications, or a related discipline.
Proficiency in data analysis and visualization tools such as Excel, Tableau, Power BI, or similar
Basic understanding of machine learning concepts and Large Language Models (LLMs)
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Define and drive Service Level Objectives (SLOs) for online machine learning inference systems, ensuring the reliability, availability, and performance of large-scale production inference services;
Ensure the reliability and operational excellence of offline machine learning training pipelines, continuously improving training job success rates;
Drive infrastructure capacity planning and resource management for machine learning workloads, ensuring compute resources meet evolving business demands while continuously improving GPU and CPU utilization through performance optimization;
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Ensure high-quality content benchmarking across BPO teams, support the setup, onboarding, and training of new BPO teams. Manage policy clarifications, calibrations, and arbitration processes. Analyze BPO team performance to identify knowledge gaps and systemic issues.
Handle content labeling for specific queues based on cross-functional business requirements, analyze labeled content to identify trends and provide insights to project teams and independently manage quality evaluation initiatives, delivering actionable analytical insights.
Train models using large datasets of labeled content to improve decision-making accuracy. Enhance model capabilities through iterative training and reinforcement learning, assist in data preparation, cleaning, and structuring for training purposes. Improve model accuracy by testing and fine-tuning, highlight the algorithm team with supporting examples for any potential gaps in LLM decision making draft, revise, and quality-check content to explore and enhance the synergy between human input and data in LLM training.
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Define and drive Service Level Objectives (SLOs) for online machine learning inference systems, ensuring the reliability, availability, and performance of large-scale production inference services;
Ensure the reliability and operational excellence of offline machine learning training pipelines, continuously improving training job success rates;
Drive infrastructure capacity planning and resource management for machine learning workloads, ensuring compute resources meet evolving business demands while continuously improving GPU and CPU utilization through performance optimization;
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