12 hours ago

Software Engineer Graduate (TikTok Recommendation Ecosystem Infrastructure) - 2026 Start (BS/MS)

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 12 hours agoStatus: Live

Job Summary

Build real-time and offline data architecture for TikTok’s recommendation ecosystem. You’ll design and implement scalable streaming Lakehouse systems for feature pipelines, model training, and real-time inference, while collaborating with ML platform teams on PyTorch-based workflows. Own core distributed storage and processing components—from file formats and stream compaction to metadata management—using technologies like Apache Flink and modern Lakehouse stacks (Paimon, Iceberg, Delta Lake, Hudi).

Location: Singapore

Workplace: Onsite

Employment Type: Full time

Job Function: Software Engineering

Seniority: Graduate level

Key Responsibilities

•Design and implement real-time and offline data architecture for large-scale recommendation systems.
•Build scalable, high-performance streaming Lakehouse systems that power feature pipelines, model training, and real-time inference.
•Collaborate with ML platform teams to support PyTorch-based model training workflows and design data formats and access patterns for large-scale samples and features.
•Own core components of the distributed storage and processing stack, from file format to stream compaction to metadata management.

Key Requirements

•Final year or recent graduate with a background in Software Development, Computer Science, Computer Engineering, or related technical fields.
•Experience building large-scale distributed systems, preferably in storage, stream processing, or ML infrastructure.
•Strong understanding of Apache Flink internals, including state management, connectors, or UDFs.
•Familiarity with Lakehouse technologies such as Apache Paimon, Iceberg, Delta Lake, or Hudi, including incremental ingestion, schema evolution, and snapshot isolation.
•Proficiency in Java/Scala/C++ with strong debugging and performance tuning ability.

Experience:Distributed systemsStorageStream processingML infrastructureLakehouseFeature pipelinesRecommendation systems

Skills:DebuggingPerformance tuningCollaboration

Tech Stack:Apache FlinkState managementConnectorsUDFsLakehouseApache PaimonIcebergDelta LakeHudiStreamingFeature pipelinesModel trainingReal-time inferencePyTorchFile formatsStream compactionMetadata managementParquetORCLance

Company Brief

TikTok

Short-form video platform that lets users create, share, and discover entertainment content through algorithmic recommendations. It also offers advertising and creator tools for brands, influencers, and businesses.

Industry: Digital Media

Company Size: Enterprise (1,001+ employees)

Growth: Scaleup

Headquarters: Singapore, Singapore

Founded: 2016

Website LinkedIn

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TikTok

12 hours ago

Software Engineer Graduate (TikTok Recommendation Ecosystem Infrastructure) - 2026 Start (BS/MS)

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 12 hours agoStatus: Live

Job Summary

Location: Singapore

Workplace: Onsite

Employment Type: Full time

Job Function: Software Engineering

Seniority: Graduate level

Key Responsibilities

•Design and implement real-time and offline data architecture for large-scale recommendation systems.
•Build scalable, high-performance streaming Lakehouse systems that power feature pipelines, model training, and real-time inference.
•Collaborate with ML platform teams to support PyTorch-based model training workflows and design data formats and access patterns for large-scale samples and features.
•Own core components of the distributed storage and processing stack, from file format to stream compaction to metadata management.

Key Requirements

•Final year or recent graduate with a background in Software Development, Computer Science, Computer Engineering, or related technical fields.
•Experience building large-scale distributed systems, preferably in storage, stream processing, or ML infrastructure.
•Strong understanding of Apache Flink internals, including state management, connectors, or UDFs.
•Familiarity with Lakehouse technologies such as Apache Paimon, Iceberg, Delta Lake, or Hudi, including incremental ingestion, schema evolution, and snapshot isolation.
•Proficiency in Java/Scala/C++ with strong debugging and performance tuning ability.

Experience:Distributed systemsStorageStream processingML infrastructureLakehouseFeature pipelinesRecommendation systems

Skills:DebuggingPerformance tuningCollaboration

Company Brief

TikTok

Industry: Digital Media

Company Size: Enterprise (1,001+ employees)

Growth: Scaleup

Headquarters: Singapore, Singapore

Founded: 2016

Website LinkedIn