Staff Software Engineer, Trends Machine Learning Infrastructure

Pinterest•San Francisco, CA

3h•$177,185 - $364,795•Hybrid

About The Position

The Trends & Insights Engineering team builds the products that turn Pinterest's unique signal what hundreds of millions of people are planning, dreaming about, and shopping for into actionable intelligence for advertisers. As a Staff Software Engineer on this team, you'll lead the technical evolution of Pinterest Trends and Audience Insights into a unified, AI-powered insights platform that shapes how advertisers plan campaigns, allocate budget, and create content across the Pinterest Ads ecosystem.

Requirements

Bachelor's degree in Computer Science, a related field, or equivalent experience.
8+ years of software engineering experience, including significant time designing large-scale data or ML-powered platforms that serve customer-facing products.
Ability to work with cross-functional partners across multiple organizations.
Hands-on experience building tools and data pipelines leveraging AI coding tools, e.g. Cursor, Claude Code, Codex, etc.
Experience as the product-engineering counterpart on an AI-first product launch from prototype to scale
Demonstrated experience using AI to accelerate engineering and analysis workflows, with a clear approach to validating accuracy, performance, and quality.
Strong track record of critical evaluation and verification of AI-assisted work — testing, source-checking, data validation, and peer review.
Experience leading cross-surface product integrations across multiple teams and organizational boundaries, not just standalone tools.

Responsibilities

Set the technical direction for a unified, audience-first insights platform that powers Pinterest Trends, Audience Insights, and recommendations embedded across Ads Manager surfaces.
Architect scalable data pipelines and systems that generate reusable, personalized insights from Pinterest's trend, audience, and content signals.
Lead delivery of high-impact roadmap bets such as Trends Digest, Moments, Topics expansion, and Product Attributes from prototype to production.
Build LLM-powered capabilities (summarization, classification, conversational insights, agentic review) with strong safety, quality, and evaluation guardrails.
Partner with Product, Design, Data Science, and Ads org teams to bring proactive, contextual insights into advertiser workflows beyond standalone surfaces.
Use AI to accelerate prototyping, design exploration, and code generation iterating across more options earlier while applying engineering judgment and verification to ensure correctness and quality.
Use AI to synthesize research, summarize large datasets, and automate repeatable engineering tasks like documentation, test generation, and data QA checks.
Mentor engineers across the team, raise the technical bar, and establish measurement practices that connect insight quality to advertiser adoption and revenue impact.