Machine Learning Engineer Intern (Training Pre-processing) - 2025 Summer (BS/MS) job opportunity at Tiktok.

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Tiktok Machine Learning Engineer Intern (Training Pre-processing) - 2025 Summer (BS/MS)
Experience: 1-year
Pattern: full-time
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ML Engineers

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degreeBachelor's (B.Eng.)
loacation SAN JOSE, CA, United States Of America
loacation SAN JOSE, CA....United States Of America

Responsibilities Team Introduction​: The TikTok Flink #Ecosystem #Team plays a critical role in delivering real-time computing capabilities to power TikTok’s massive-scale recommendation, search, and advertising systems. This team is focused on building the infrastructure for stream processing at exabyte scale — enabling ultra-low-latency, high-reliability, and cost-efficient real-time data transformations.​ We are deeply involved in developing and optimizing Apache Flink and surrounding components like connectors, state backends, and runtime execution models to meet TikTok’s rapidly evolving data needs at EB-level throughput and scale.​ ​ We also collaborate closely with ML infrastructure teams to bridge real-time stream processing and machine learning. This includes integrating Velox to accelerate model training, building multimodal data pipelines, and utilizing frameworks like Ray to orchestrate large-scale distributed ML workflows. Responsibilities: ​ Design and develop core Flink operators, connectors, or runtime modules to support TikTok’s exabyte-scale real-time processing needs.​ ​- Build and maintain low-latency, high-throughput streaming pipelines powering online learning, recommendation, and ranking systems.​ ​Collaborate with #ML #engineers to design end-to-end real-time ML pipelines, enabling efficient feature generation, training data streaming, and online inference.​ Leverage Velox for compute-optimized ML #data #transformation and training acceleration on multimodal datasets (e.g., video, audio, and text).​ Use Ray to coordinate distributed machine learning workflows and integrate real-time feature pipelines with ML model training/inference.​ Optimize Flink job performance, diagnose bottlenecks, and deliver scalable solutions across EB-scale streaming workloads.

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