Staff Software Engineer, Trends Machine Learning Infrastructure

What you’ll do

• 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.

What we’re looking for

• 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.