AI Feature/Computing/Storage Engineer Graduate (TikTok Recommendation Ecosystem Architecture) - 2027 Start
Tasks
- Build scalable streaming lakehouse systems for feature pipelines model training and real time inference
- Collaborate with ML platform teams to support PyTorch model training workflows
- Design data formats and access patterns for large scale samples and features
- Design real-time and offline data architecture for recommendation systems
- Implement incremental ingestion schema evolution and snapshot isolation
- Manage lakehouse metadata compaction scheduling and data versioning
- Optimize Flink and Lakehouse architectures for unified batch and stream processing
- Own distributed storage and processing stack components from file format to stream compaction to metadata management
Perks/Benefits
- N/A
Skills/Tech-stack
Apache Flink | Apache Hudi | Apache Iceberg | Apache Paimon | C++ | Connectors | Data Versioning | Delta Lake | Distributed Systems | Feature Engineering | Incremental ingestion | Java | Lakehouse | ORC | Parquet | PyTorch | Scala | Schema evolution | Snapshot Isolation | State management | Streaming | User Defined Functions
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
Roles
AI | AI Engineer | Data Engineer | Engineer
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