Senior Software Engineer - Ads Experimentation Platform
SCRAPED
Used Tools & Technologies
Not specified
Required Skills & Competences ?
Docker @ 4 ElasticSearch @ 4 Go @ 4 Kafka @ 4 Kubernetes @ 4 Redis @ 4 Scala @ 4 A/B Testing @ 4 Spark @ 4 Statistics @ 4 GCP @ 4 Airflow @ 4 Flink @ 4 Data Science @ 4 Hiring @ 4 AWS @ 4 Mathematics @ 4 Protobuf @ 4 API @ 4 Experimentation @ 4 Cassandra @ 4Details
Reddit is a community of communities. It’s built on shared interests, passion, and trust and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. Reddit’s revenue primarily comes from advertising, and the Ads Experimentation Platform (AEXP) team builds tools for engineers and data scientists to accurately test new ad products and verify ideas in the ads marketplace.
The Ads Experimentation Platform tackles problems such as budget cannibalization, limitations of user-split A/B testing in marketplaces, extracting signals from rare/sparse conversion events, and running large-scale exploration across many algorithmic candidates. The team also builds scalable ad pacing and budgeting infrastructure to support marketplace dynamism and budget-aware experiments.
Responsibilities
- Build A/B testing tools customized for the Ads marketplace.
- Build A/B testing tools for measuring infrastructure cost by each ad product.
- Build advertiser-facing tools for exploring Reddit Ads products and optimizing performance.
- Build a scalable and reliable ad pacing platform to enable efficient experimentation and algorithm research.
- Collaborate closely with data science partners, product leaders, and cross-functional engineering teams to improve experimentation capacity and practices across Ads marketplace teams.
Requirements / Qualifications
Who you might be:
- Degree in a quantitative discipline (engineering, statistics, operations research, computer science, informatics, applied mathematics, economics, etc.).
- 5+ years contributing high-quality code to production systems operating at scale.
- 3+ years of experience building ads-serving related systems (ads targeting, ranking, pacing, etc.).
- Experience building A/B testing frameworks for multiparty marketplace scenarios (e.g., food delivery, ride sharing, ads marketplace).
- Experience leading large engineering teams and collaborating with cross-functional partners, especially data science.
Required technical qualifications (technologies referenced):
- Backend programming languages: significant experience; experience with Go or Scala is preferable.
- API development and service frameworks: Thrift, Protobuf.
- Data processing / streaming frameworks: Spark, Flink, Kafka, Druid.
- Cloud: AWS or GCP.
- Tools / orchestration / CI: Kubernetes, Drone, CircleCI, Spinnaker, Argo, Airflow, Docker.
- Datastores: ElasticSearch / Amazon OpenSearch, Redis, Postgres, Cassandra, BigQuery.
Benefits
- Comprehensive healthcare benefits and income replacement programs.
- 401(k) match.
- Family planning support.
- Gender-affirming care.
- Mental health & coaching benefits.
- Flexible vacation & Reddit global days off.
- Generous paid parental leave.
- Paid volunteer time off.
Pay Transparency
- This job posting may span more than one career level and is eligible for equity (RSUs) and, depending on role, commission.
- Base pay range for US-based candidates: $190,800 - $267,100 USD.
- Final offer amounts are determined by skills, experience, and other factors.
Interview & Privacy Notes
- In select roles and locations, interviews may be recorded, transcribed, and summarized by AI; candidates will be able to opt out prior to scheduled interviews.
- During interviews Reddit may collect identifiers, professional/employment information, sensory information (audio/video), and other information you choose to share; recordings are deleted after a hiring decision per the Candidate Privacy Policy.
Additional Notes
- Role works closely with data science and product teams to design experimentation methodologies and tools for the Ads marketplace.
- Emphasis on scalable, reliable systems and methods for marketplace experimentation, pacing, and budget-aware testing.