Software Engineer Graduate (Data Arch - Data Ecosystem ) - 2026 (PhD)
Jul 18, 2025 San Jose
About us
TikTok is the leading destination for short-form mobile video and our mission is to inspire creativity and bring joy.
- Size: 10,000+ employees
- Industry: Technology: Consumer Internet
Why you should apply for a job to TikTok:
4.5/5 in overall job satisfaction
4.5/5 in supportive management
100% say women are treated fairly and equally to men
100% would recommend this company to other women
100% say the CEO supports gender diversity
Ratings are based on anonymous reviews by Fairygodboss members. See all reviews ›
Responsibilities
About the team:
The TikTok Data Ecosystem Team has the vital role of crafting and implementing a storage solution for offline data in TikTok's recommendation system, which caters to more than a billion users. Their primary objectives are to guarantee system reliability, uninterrupted service, and seamless performance. They aim to create a storage and computing infrastructure that can adapt to various data sources within the recommendation system, accommodating diverse storage needs. Their ultimate goal is to deliver efficient, affordable data storage with easy-to-use data management tools for the recommendation, search, and advertising functions.
We are looking for talented individuals to join our team in 2026. As a graduate, you will get unparalleled opportunities for you to kickstart your career, pursue bold ideas and explore limitless growth opportunities. Co-create a future driven by your inspiration with TikTok.
Successful candidates must be able to commit to an onboarding date by end of year 2026.
Responsibilities:
1. Design and implement real-time and offline data architecture for large-scale recommendation systems.
2. Build scalable and high-performance streaming Lakehouse systems that power feature pipelines, model training, and real-time inference.
3. Collaborate with ML platform teams to support PyTorch-based model training workflows and design efficient data formats and access patterns for large-scale samples and features.
4. Own core components of our distributed storage and processing stack, from file format to stream compaction to metadata management.
Qualifications
Minimum Qualifications:
- Individuals who are completing or have recently completed a PhD 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, with hands-on experience in state management, connectors, or UDFs.
- Familiarity with modern Lakehouse technologies such as Apache Paimon, Iceberg, Delta Lake, or Hudi, especially around incremental ingestion, schema evolution, and snapshot isolation.
Preferred Qualifications:
- Experience in designing and optimizing Flink + Paimon architectures for unified batch/stream processing.
- Familiarity with feature storage and training data pipelines, and their integration with PyTorch, especially for large-scale model training.
- Knowledge of columnar file formats (Parquet, ORC, Lance) and how they are used in feature engineering or ML data loading.
- Proficiency in Java/Scala/C++, and strong debugging/performance tuning ability.
- Previous experience in Lakehouse metadata management, compaction scheduling, or data versioning is a plus.
- (Optional) Knowledge of legacy data stores like HBase/Kudu is a bonus but not required.
Job Information
[For Pay Transparency] Compensation Description (annually)
The base salary range for this position in the selected city is $128000 - $316800 annually.
Compensation may vary outside of this range depending on a number of factors, including a candidate's qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.
Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).
The Company reserves the right to modify or change these benefits programs at any time, with or without notice.
For Los Angeles County (unincorporated) Candidates:
Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:
1. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;
2. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and
3. Exercising sound judgment.
About the team:
The TikTok Data Ecosystem Team has the vital role of crafting and implementing a storage solution for offline data in TikTok's recommendation system, which caters to more than a billion users. Their primary objectives are to guarantee system reliability, uninterrupted service, and seamless performance. They aim to create a storage and computing infrastructure that can adapt to various data sources within the recommendation system, accommodating diverse storage needs. Their ultimate goal is to deliver efficient, affordable data storage with easy-to-use data management tools for the recommendation, search, and advertising functions.
We are looking for talented individuals to join our team in 2026. As a graduate, you will get unparalleled opportunities for you to kickstart your career, pursue bold ideas and explore limitless growth opportunities. Co-create a future driven by your inspiration with TikTok.
Successful candidates must be able to commit to an onboarding date by end of year 2026.
Responsibilities:
1. Design and implement real-time and offline data architecture for large-scale recommendation systems.
2. Build scalable and high-performance streaming Lakehouse systems that power feature pipelines, model training, and real-time inference.
3. Collaborate with ML platform teams to support PyTorch-based model training workflows and design efficient data formats and access patterns for large-scale samples and features.
4. Own core components of our distributed storage and processing stack, from file format to stream compaction to metadata management.
Minimum Qualifications:
- Individuals who are completing or have recently completed a PhD 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, with hands-on experience in state management, connectors, or UDFs.
- Familiarity with modern Lakehouse technologies such as Apache Paimon, Iceberg, Delta Lake, or Hudi, especially around incremental ingestion, schema evolution, and snapshot isolation.
Preferred Qualifications:
- Experience in designing and optimizing Flink + Paimon architectures for unified batch/stream processing.
- Familiarity with feature storage and training data pipelines, and their integration with PyTorch, especially for large-scale model training.
- Knowledge of columnar file formats (Parquet, ORC, Lance) and how they are used in feature engineering or ML data loading.
- Proficiency in Java/Scala/C++, and strong debugging/performance tuning ability.
- Previous experience in Lakehouse metadata management, compaction scheduling, or data versioning is a plus.
- (Optional) Knowledge of legacy data stores like HBase/Kudu is a bonus but not required.
About the team:
The TikTok Data Ecosystem Team has the vital role of crafting and implementing a storage solution for offline data in TikTok's recommendation system, which caters to more than a billion users. Their primary objectives are to guarantee system reliability, uninterrupted service, and seamless performance. They aim to create a storage and computing infrastructure that can adapt to various data sources within the recommendation system, accommodating diverse storage needs. Their ultimate goal is to deliver efficient, affordable data storage with easy-to-use data management tools for the recommendation, search, and advertising functions.
We are looking for talented individuals to join our team in 2026. As a graduate, you will get unparalleled opportunities for you to kickstart your career, pursue bold ideas and explore limitless growth opportunities. Co-create a future driven by your inspiration with TikTok.
Successful candidates must be able to commit to an onboarding date by end of year 2026.
Responsibilities:
1. Design and implement real-time and offline data architecture for large-scale recommendation systems.
2. Build scalable and high-performance streaming Lakehouse systems that power feature pipelines, model training, and real-time inference.
3. Collaborate with ML platform teams to support PyTorch-based model training workflows and design efficient data formats and access patterns for large-scale samples and features.
4. Own core components of our distributed storage and processing stack, from file format to stream compaction to metadata management.
Qualifications
Minimum Qualifications:
- Individuals who are completing or have recently completed a PhD 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, with hands-on experience in state management, connectors, or UDFs.
- Familiarity with modern Lakehouse technologies such as Apache Paimon, Iceberg, Delta Lake, or Hudi, especially around incremental ingestion, schema evolution, and snapshot isolation.
Preferred Qualifications:
- Experience in designing and optimizing Flink + Paimon architectures for unified batch/stream processing.
- Familiarity with feature storage and training data pipelines, and their integration with PyTorch, especially for large-scale model training.
- Knowledge of columnar file formats (Parquet, ORC, Lance) and how they are used in feature engineering or ML data loading.
- Proficiency in Java/Scala/C++, and strong debugging/performance tuning ability.
- Previous experience in Lakehouse metadata management, compaction scheduling, or data versioning is a plus.
- (Optional) Knowledge of legacy data stores like HBase/Kudu is a bonus but not required.
Job Information
[For Pay Transparency] Compensation Description (annually)
The base salary range for this position in the selected city is $128000 - $316800 annually.
Compensation may vary outside of this range depending on a number of factors, including a candidate's qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units.
Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure).
The Company reserves the right to modify or change these benefits programs at any time, with or without notice.
For Los Angeles County (unincorporated) Candidates:
Qualified applicants with arrest or conviction records will be considered for employment in accordance with all federal, state, and local laws including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Our company believes that criminal history may have a direct, adverse and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment:
1. Interacting and occasionally having unsupervised contact with internal/external clients and/or colleagues;
2. Appropriately handling and managing confidential information including proprietary and trade secret information and access to information technology systems; and
3. Exercising sound judgment.
Responsibilities
ResponsibilitiesAbout the team:
The TikTok Data Ecosystem Team has the vital role of crafting and implementing a storage solution for offline data in TikTok's recommendation system, which caters to more than a billion users. Their primary objectives are to guarantee system reliability, uninterrupted service, and seamless performance. They aim to create a storage and computing infrastructure that can adapt to various data sources within the recommendation system, accommodating diverse storage needs. Their ultimate goal is to deliver efficient, affordable data storage with easy-to-use data management tools for the recommendation, search, and advertising functions.
We are looking for talented individuals to join our team in 2026. As a graduate, you will get unparalleled opportunities for you to kickstart your career, pursue bold ideas and explore limitless growth opportunities. Co-create a future driven by your inspiration with TikTok.
Successful candidates must be able to commit to an onboarding date by end of year 2026.
Responsibilities:
1. Design and implement real-time and offline data architecture for large-scale recommendation systems.
2. Build scalable and high-performance streaming Lakehouse systems that power feature pipelines, model training, and real-time inference.
3. Collaborate with ML platform teams to support PyTorch-based model training workflows and design efficient data formats and access patterns for large-scale samples and features.
4. Own core components of our distributed storage and processing stack, from file format to stream compaction to metadata management.
Qualifications
QualificationsMinimum Qualifications:
- Individuals who are completing or have recently completed a PhD 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, with hands-on experience in state management, connectors, or UDFs.
- Familiarity with modern Lakehouse technologies such as Apache Paimon, Iceberg, Delta Lake, or Hudi, especially around incremental ingestion, schema evolution, and snapshot isolation.
Preferred Qualifications:
- Experience in designing and optimizing Flink + Paimon architectures for unified batch/stream processing.
- Familiarity with feature storage and training data pipelines, and their integration with PyTorch, especially for large-scale model training.
- Knowledge of columnar file formats (Parquet, ORC, Lance) and how they are used in feature engineering or ML data loading.
- Proficiency in Java/Scala/C++, and strong debugging/performance tuning ability.
- Previous experience in Lakehouse metadata management, compaction scheduling, or data versioning is a plus.
- (Optional) Knowledge of legacy data stores like HBase/Kudu is a bonus but not required.
Additional Information
- Salary: $128,000 – $316,800
- Posted: Jul 18, 2025