Zefr operates a technology stack built for programmatic video and brand verification across Meta, TikTok, and YouTube — anchored on Salesforce, Snowflake, Kafka, and dbt for data pipeline automation. The hiring mix is tilted toward data (3 roles) and marketing (4 roles), with engineering (2 roles) a distant third, reflecting a business model centered on campaign execution and client onboarding rather than core product development. Active pain points reveal operational friction: manual reporting overhead, client onboarding delays, and compliance gaps in pharma advertising — all addressable through internal tooling, not platform innovation.
Zefr provides technology and verification services for brand-safe marketing across social walled gardens like Meta, TikTok, and YouTube. The platform combines contextual video targeting, brand suitability scoring, and measurement capabilities aimed at agencies and advertisers managing programmatic spend at scale. Founded in 2009, Zefr operates from Los Angeles with 201–500 employees and maintains active hiring across the United States, Singapore, the United Kingdom, and Israel. The product roadmap focuses on reducing friction in client onboarding, post-campaign reporting, and compliance workflows — particularly for regulated verticals such as pharmaceuticals — while building bespoke reporting and measurement modules for individual clients.
Zefr uses Salesforce, HubSpot, Snowflake, Apache Kafka, and dbt for core operations and data pipelines. Engineering uses Python, FastAPI, Docker, Kubernetes, and GitHub on AWS infrastructure.
Zefr has 2 active engineering roles in its hiring pipeline. The company is hiring across the United States, Singapore, United Kingdom, and Israel.
Current projects include campaign measurement and analysis, custom reporting development, client onboarding structures, post-test reporting, and workflow optimization with AI agents.
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