About the Job

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).