Software Engineer Graduate (Data Arch - Data Ecosystem ) - 2026 Start (BS/MS) job opportunity at Tiktok.

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Tiktok Software Engineer Graduate (Data Arch - Data Ecosystem ) - 2026 Start (BS/MS)
Experience: 1-year
Pattern: full-time
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Software Engineer

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degreeBachelor's (B.Sc.)
loacation SAN JOSE, CA, United States Of America
loacation SAN JOSE, CA....United States Of America

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 opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Launch your career where inspiration is infinite at TikTok. Successful candidates must be able to commit to an onboarding date by end of year 2026. Please state your availability and graduation date clearly in your resume. Candidates can apply for a maximum of TWO positions and will be considered for jobs in the order you applied for. The application limit is applicable to TikTok and its affiliates' jobs globally. Applications will be reviewed on a rolling basis - we encourage you to apply early. 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.

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