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Sales Engineer, tvScientific

Job Overview

Location

San Francisco, CA, US; Remote, US

Job Type

Full-time

Category

Sales

Date Posted

April 14, 2026

Full Job Description

đź“‹ Description

  • • As a Sales Engineer at tvScientific (a Pinterest company), you will serve as the primary technical expert guiding clients through the full lifecycle of CTV advertising platform adoption—from initial discovery to post-launch success—ensuring seamless integration and measurable business outcomes.
  • • Day to day, you will partner with Sales to design and validate client integrations, lead discovery conversations around pixels, server-to-server postbacks, and custom reporting, troubleshoot implementation issues, translate technical concepts into clear client explanations, and collaborate with Product, Engineering, and Customer Success to relay feedback and shape solutions.
  • • You’ll join a mission-driven team at Pinterest, where AI augments creativity and innovation, working within tvScientific—the first and only CTV advertising platform purpose-built for performance marketers—leveraging massive data and cutting-edge science to automate and optimize TV advertising.
  • • In this role, you’ll deepen your expertise in ad tech, measurement, and attribution while building trusted client relationships, solving complex technical challenges, and contributing to the evolution of a high-impact performance advertising platform used by enterprise advertisers.

Skills & Technologies

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Pinterest, Inc.
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About Pinterest, Inc.

Pinterest operates a visual discovery platform where users save and share images linked to recipes, home ideas, style inspiration, and other interests. Founded in 2010 and headquartered in San Francisco, the company provides free accounts, visual search tools, and shoppable pins that connect consumers with retailers. Revenue comes primarily from native advertising that appears within users’ feeds and search results. The service is accessible via web and mobile apps worldwide, emphasizing personalized recommendations driven by machine-learning models trained on user activity and image data.

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