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# Machine Learning Engineer, Data Mining (Ads Core)

- **Company:** Tiktok Inc
- **Location:** San Jose, CA, United States
- **Experience:** Experienced
- **Salary:** $162,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Airflow, Big Data, Information Engineering, Data Governance, Extract Transform Load (ETL), Data Mining, Apache Hive, Python (Programming Language), Machine Learning, Performance Tuning, Recommender Systems, Rule Engine, Standard Sql, System Availability, Apache Spark, Caching, Low Latency, Apache Flink, Apache Kafka, Live Streaming, Data Management
- **Published:** October 2, 2026
- **Apply:** https://www.themuse.com/jobs/tiktok/machine-learning-engineer-data-mining-ads-core

## About the Role

BS+ in CS/SE/Data Engineering or related fields

 - 3+ years (adjustable) in data engineering/platform roles; able to own critical pipelines end-to-end
 - Strong SQL and data modeling; hands-on with big data stack (Spark/Hive/Kafka/Flink/Airflow, etc.)
 - Proficient in Java/Scala/Python; solid engineering and performance tuning skills
 - Strong ownership of data governance, definitions, quality and stability
 - Effective cross-functional communication and execution

Preferred Qualifications:

 - Experience in ads/recommender data platforms: user profiles, tagging, audience segmentation, DMP/CDP
 - Real-time profile or low-latency serving at scale (high QPS, caching/consistency)
 - Privacy/compliance implementation experience (minimization, anonymization, access control, auditing)

## Description

Monetization Technology teams are building the next-generation monetization platforms to help millions of customers grow their businesses, utilizing our products like TikTok. Our team develops a wide variety of advertisements for numerous uses including feeds, live streaming, branding, measurement, targeting, search, vertical solutions, creative solutions, and business integrity.

What You'll Do:

 - Own foundational targeting data and platform capabilities with high availability, accuracy, freshness, and scalability:
 - Base targeting dimensions: gender, age, geo, device, language, network, etc.
 - Audience & tagging system: definitions, hierarchy, refresh strategy, backfills, cross-device unification
 - Design and implement large-scale batch/stream pipelines: ingestion, ETL, aggregation, profile generation, tag updates, external serving
 - Build a reliable data quality framework: validation, lineage, monitoring/alerting, SLAs, automated backfill and repair
 - Provide standardized capabilities for ads delivery/strategy systems:
 - Audience package generation/management, tag query services, foundational targeting rule engine, access control & auditing
 - Collaborate with ML/product/compliance to ensure stable production rollout and iterative improvements (performance/reach/cost/UX)


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