ByteDance

1 day ago

Software Engineer Graduate (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: 23 hours agoStatus: Live

Job Summary

Build the data ecosystem infrastructure behind large-scale recommendation systems used by over a billion users. Design and implement real-time and offline data architectures, including streaming Lakehouse systems for feature pipelines, model training, and real-time inference. Partner with ML platform teams to enable PyTorch-based training workflows and own core distributed storage/processing components such as file formats, stream compaction, and metadata management.

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 powering feature pipelines, model training, and real-time inference.
•Collaborate with ML platform teams to support PyTorch-based model training workflows and design efficient data formats/access patterns.
•Own core components of a distributed storage and processing stack, including file format, stream compaction, and metadata management.

Key Requirements

•Completing or recently completing a Bachelor’s/ Master’s degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
•Experience building large-scale distributed systems, preferably in storage, stream processing, or ML infrastructure.
•Solid understanding of Apache Flink internals, including hands-on experience with state management, connectors, or UDFs.
•Familiarity with modern Lakehouse technologies such as Apache Paimon, Iceberg, Delta Lake, or Hudi, including incremental ingestion, schema evolution, and snapshot isolation.
•Proficiency in Java/Scala/C++ and strong debugging/performance tuning ability.

Experience:Distributed systemsStorageStream processingML infrastructureLakehouseData engineering

Education:

Tech Stack:Apache FlinkPyTorchLakehouseApache PaimonIcebergDelta LakeHudiParquetORCLanceJavaScalaC++UDFsState managementConnectorsStream processingIncremental ingestionSchema evolutionSnapshot isolation

Company Brief

ByteDance

Develops consumer internet and content platforms, including TikTok and other apps for short-form video, news, and entertainment. It also builds advertising, commerce, and creator tools that connect audiences, brands, and publishers across global markets.

Industry: Digital Media

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Established Company

Headquarters: Beijing, China

Founded: 2012

Website LinkedIn

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ByteDance

1 day ago

Software Engineer Graduate (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: 23 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 powering feature pipelines, model training, and real-time inference.
•Collaborate with ML platform teams to support PyTorch-based model training workflows and design efficient data formats/access patterns.
•Own core components of a distributed storage and processing stack, including file format, stream compaction, and metadata management.

Key Requirements

•Completing or recently completing a Bachelor’s/ Master’s degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline.
•Experience building large-scale distributed systems, preferably in storage, stream processing, or ML infrastructure.
•Solid understanding of Apache Flink internals, including hands-on experience with state management, connectors, or UDFs.
•Familiarity with modern Lakehouse technologies such as Apache Paimon, Iceberg, Delta Lake, or Hudi, including incremental ingestion, schema evolution, and snapshot isolation.
•Proficiency in Java/Scala/C++ and strong debugging/performance tuning ability.

Experience:Distributed systemsStorageStream processingML infrastructureLakehouseData engineering

Education:

Company Brief

ByteDance

Industry: Digital Media

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Established Company

Headquarters: Beijing, China

Founded: 2012

Website LinkedIn