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

s 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.

Qualifications

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 (e.g., combining video, audio, and text in model training pipelines).
• Experience working with data lake ecosystems (e.g., 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.