Ads Core team is chartered to build key monetization components across various ad delivery stages:
- We build up ranking, bidding, budget, format, diagnosis and other frameworks that serve as a mid platform to enable other ad teams to iterate their products in parallel.
- We implement outstanding traffic strategies to maximize revenue under the constraint of user experience and achieve complete exploration of advertiser's audience.
- Our model driven automation solutions optimize ad delivery performance from end to end.
As a Project Intern, you will contribute to impactful short-term projects and gain hands-on experience in a fast-paced, professional environment. This internship offers the opportunity to develop practical skills, apply your knowledge to real-world challenges, and explore your career interests. Applications are reviewed on a rolling basis, so we encourage you to apply early.
Responsibilities
- Help build foundational targeting data and platform capabilities, including:
- Gender, age buckets, geo targeting (country/region/city/POI/lat-long), device/OS, language, etc.
- Assist in user profile/targeting tag pipelines: ingestion, cleaning, definition alignment, tag generation and refresh.
- Develop batch and streaming jobs (depending on stack): offline (Hive/Spark), real-time (Flink/Kafka).
- Improve data quality: consistency checks, latency monitoring, anomaly alerting, backfill and remediation.
- Provide stable data services to ads delivery/strategy systems: audience packages, tag query, targeting rule parsing (with mentorship).
Minimum Qualifications:
- Currently pursuing a Undergraduate/ Master's in CS/Data Engineering/IS or or a related discipline.
- Strong SQL and solid data modeling fundamentals.
- Proficient in at least one language: Java/Scala/Python.
- Familiar with parts of big data ecosystem (Hive/Spark/Flink/Kafka/Airflow) is a plus.
- Detail-oriented; comfortable working on data definitions and quality governance.
Preferred Qualifications:
- Experience with user profiles, tagging systems, DMP/CDP, ads or recommender data pipelines.
- Exposure to privacy, anonymization, access control (as needed).