Staff Machine Learning Engineer - Search Quality

at  Pinterest

About Pinterest:
Millions of people across the world come to Pinterest to find new ideas every day. It’s where they get inspiration, dream about new possibilities and plan for what matters most. Our mission is to help those people find their inspiration and create a life they love. In your role, you’ll be challenged to take on work that upholds this mission and pushes Pinterest forward. You’ll grow as a person and leader in your field, all the while helping Pinners make their lives better in the positive corner of the internet.
Creating a life you love also means finding a career that celebrates the unique perspectives and experiences that you bring. As you read through the expectations of the position, consider how your skills and experiences may complement the responsibilities of the role. We encourage you to think through your relevant and transferable skills from prior experiences.
Our new progressive work model is called PinFlex, a term that’s uniquely Pinterest to describe our flexible approach to living and working. Visit our PinFlex landing page to learn more.
Pinterest helps Pinners discover and do what they love. At the core of this discovery experience is Pinterest search. The Search Quality team is focused on making Pinterest search fast, personalized, and relevant for the billions of monthly searches. We seek a Tech Lead for ranking who can drive cross-team engineering efforts to deliver ML-driven product experiences to our Pinners. This role offers the opportunity to work on innovative projects, build large-scale low-latency systems and state-of-the-art machine learning models, and significantly impact our Pinners and business metrics.

• Design and build ML models that understand user intent and deliver the most relevant pins.
• Utilize state-of-the-art modeling techniques to improve Pinterest search.
• Improve the efficiency and reliability of large-scale data processing and ML inference pipelines.
• Optimize our serving systems to improve latency, capacity, and stability and reduce infrastructure costs.