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Urgent! Machine Learning Engineer Intern (Training Pre-processing) - 2025 Summer (PhD) Job Opening In San Jose – Now Hiring TikTok

Machine Learning Engineer Intern (Training Pre processing) 2025 Summer (PhD)



Job description

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 (., 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.



Minimum Qualifications:
- Currently pursuing a PhD’s degree in Computer Science, Software Engineering, Data Engineering, or a related technical field.
- Strong programming skills in Java, Scala, or Python.
- Understanding of distributed systems, stream processing, and event-driven architecture.
- Familiar with system design concepts such as fault tolerance, backpressure, and horizontal scalability.
- Demonstrated ability to debug and analyze complex distributed jobs in production environments.

Preferred Qualifications:
- Graduating in December 2025 or later, with the intent to return to your academic program.
- Experience with Apache Flink, Spark Streaming, or Kafka Streams.
- Hands-on experience with Ray for distributed ML or workflow orchestration.
- Familiarity with Velox, Arrow, or similar columnar execution engines for training/feature pipelines.
- Understanding of multimodal data processing (., combining video, audio, and text in model training pipelines).
- Experience working with data lake ecosystems (., Iceberg, Hudi, Delta Lake) and cloud-native storage at PB–EB scale.
- Contributions to open-source projects or participation in ML/engineering hackathons or competitions.


Required Skill Profession

Computer Occupations



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