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This position was posted on April 23, 2026 and is likely no longer accepting applications. We've kept it here for historical reference. Check out the similar jobs below!

Pinterest, Inc. logo

Sr. ML Platform Engineer, tvScientific

Job Overview

Location

San Francisco, CA, US; Remote, US

Job Type

Full-time

Category

Backend Engineer

Date Posted

April 23, 2026

Full Job Description

📋 Description

  • As a Sr. ML Platform Engineer at tvScientific (part of Pinterest), you will play a critical role in scaling and optimizing the machine learning infrastructure that powers real-time bidding and model training systems driving over $100M in annual revenue.
  • Your day-to-day responsibilities include scaling decision-making tools for AI workflows, improving developer experience for data science teams, upgrading observability tooling, serving as a technical lead and mentor, and ensuring smooth deployments as infrastructure evolves.
  • You will join a team at the intersection of systems programming, architecture, and large-scale deployments, working on business-critical infrastructure that combines media buying, optimization, measurement, and attribution in the CTV advertising space.
  • This role offers the opportunity to shape the next generation of training and serving stacks using Kubernetes and Ray, while gaining deep expertise in distributed systems, data locality, high-performance computing, and ML reproducibility in a high-impact, performance-driven environment.

Skills & Technologies

Python
Scala
Kubernetes
Terraform
Linux
Senior
Remote

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Pinterest, Inc. logo
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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