Machine Learning Engineer, Data Mining (Ads Core)

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)

Qualifications

Minimum Qualifications

• 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)