Technology
StaffSoftwareEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Staff Software Engineer at Pinterest”
Industry & Context.
Root cause analysis; Troubleshooting
In-office 1x quarter, Commutable distance
How You'll Work.
Communication Scope
Executive presentations
Full Job Description
About Pinterest: Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product. Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible. At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI. Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here. Pinterest's Growth org reaches hundreds of millions of users by helping them discover Pinterest from across the web — through search engines, generative engines, and other emerging surfaces. We're hiring a Staff Software Engineer to be the product engineering anchor across two closely partnered teams: our SEO Product team, which owns Pinterest's durable organic discovery channels, and a new exploratory team focused on AI-first growth products. This is a hands-on technical role for a strong product engineer who wants to architect and ship AI-powered growth experiences at scale, partnering closely with ML teams to take capabilities from prototype to production. You'll set the technical bar through your own code and architecture — not by stepping into management. What you'll do: Architect and build the production systems that AI/ML capabilities ship throu
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