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Machine Learning Engineer Intern (FeatureStore) - 2025 Summer (PhD) Job Opening In San Jose – Now Hiring TikTok


Job description

Team Introduction

:The TikTok Data Ecosystem Team plays a critical role in supporting TikTok’s personalized recommendation system, which serves over 1 billion users.

We are responsible for building scalable, reliable, and high-performance infrastructure for storing and serving machine learning features — especially user behavior sequences and contextual embeddings used in large-scale recommendation and pretraining models.

Our work sits at the intersection of systems and machine learning: ensuring training-serving consistency, low-latency access to temporal features, and scalable ingestion pipelines across online and offline environments.

We explore and integrate with various underlying storage engines, including RocksDB, HBase, and time-series databases, depending on the access pattern, feature type, and serving latency required by ML models.

Responsibilities:
- Build and optimize the core infrastructure of TikTok’s feature store, powering both training data pipelines and real-time inference systems.
- Design efficient storage strategies for user behavior sequences, long-range contextual features, and sparse embeddings — ensuring freshness, consistency, and high availability.
- Work with underlying storage engines such as RocksDB, HBase, and time-series databases to support feature retention, versioning, compaction, and fast lookup.
- Collaborate with recommendation algorithm teams to design schemas and access patterns tailored to evolving model needs.
- Integrate online and offline data pipelines to reduce training-serving skew and support continuous training and A/B testing scenarios.
- Investigate techniques such as temporal sampling, embedding quantization, caching, and hybrid tiered storage to improve cost-efficiency and latency.



Minimum Qualifications​:
​- Currently pursuing a PhD’s degree or above in Computer Science, Software Engineering, or a related technical field.​ - Solid foundation in distributed systems, data storage, and stream/batch processing architectures.​ - Experience in programming with Java, C++, or Python.​ - Understanding of key-value stores, LSM-tree architectures, or time-series databases at a system level.​ - Eagerness to work on ambiguous, real-world infrastructure problems that impact ML product outcomes.​ Preferred Qualifications​:
- Graduating in December 2025 or later with intent to return to your program.​ - Experience working with RocksDB, HBase, or time-series storage engines like IoTDB, OpenTSDB, or custom LSM-tree variants.​ - Familiarity with feature store design, feature lifecycle management, and streaming ingestion pipelines.​ - Understanding of recommendation system workflows, such as two-tower models, real-time CTR prediction, or user intent modeling.​ - Contributions to open-source storage/ML infra projects or participation in ML system hackathons.

Required Skill Profession

Computer Occupations


  • Job Details

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