
Job Overview
Location
Toronto, ON, CA
Job Type
Full-time
Category
Android Developer
Date Posted
April 14, 2026
Full Job Description
đź“‹ Description
- • As a Sr. Software Engineer, Android at Pinterest, you will play a critical role in building and enhancing core Android experiences used by millions of users worldwide, enabling both end-users and fellow developers through robust, scalable features and frameworks.
- • Day to day, you will support millions of users by ensuring excellence in shared application components, identify and solve app-wide challenges, drive improvements in modularization, image/video loading, RTL text, dependency injection, and reusable UI components, enhance app architecture and testing capabilities, solve performance and crash-related issues, and leverage AI to accelerate prototyping, automate documentation, and improve code quality.
- • You will join Pinterest’s platform Engineering team in Toronto, collaborating closely with product managers, designers, and backend engineers to deliver high-impact innovations, with the flexibility to work remotely while attending in-person collaboration sessions 1-2 times per quarter at the Toronto office.
- • In this role, you will grow as a developer by working on varied, high-impact projects with world-class peers, deepen your expertise in Android best practices, strengthen your ability to critically evaluate AI-assisted work, and take ownership of end-to-end feature delivery from inception to release.
Skills & Technologies
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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