Lead and grow a team of engineers building Pinterest Trends, Audience Insights, and the next generation of AI-powered advertiser insights products.
Translate the team's vision into a cohesive roadmap and delivery plan, sequencing key 2026 bets such as Trends Digest, Moments, Topics expansion, Product Attributes, and the audience-first platform foundation.
Drive measurable advertiser impact by establishing a North Star tied to adoption, insight quality, and incremental ad spend and by standing up the instrumentation needed to prove it.
Partner closely with Product, Design, Data Science, and peer Ads org teams to embed insights and recommendations into Ads Manager, seller workflows, and other advertiser journeys.
Set the technical bar for LLM-powered capabilities (summarization, classification, conversational insights, agentic review) with strong safety, quality, and evaluation guardrails.
Hire, mentor, and retain senior and staff engineering talent, and build team operating rhythms (planning, reviews, on‑call, quality bars) that scale with the charter.
Use AI to accelerate planning, technical exploration, and decision-making across the team drafting strategy docs, comparing architectural approaches, and synthesizing research faster while applying judgment and verification to ensure correctness and quality.
Use AI to automate repeatable management tasks like status reporting, document synthesis, and review workflows, freeing the team to focus on higher‑leverage work.
Requirements
Bachelor's degree in Computer Science, a related field, or equivalent experience.
8+ years of software engineering experience, including 3+ years managing teams of 5+ engineers.
Experience leading engineering teams that build and ship consumer‑facing products.
Experience managing engineers across multiple levels and helping teams improve in effectiveness, quality, and impact.
Strong people leadership skills, including coaching, feedback, performance management, and team development.
Strong product sense and strategic thinking, with the ability to translate broad goals into clear priorities and execution plans.
Solid technical foundation and the ability to engage in architecture, design reviews, and technical trade‑offs.
Proven ability to lead through ambiguity, align cross‑functional stakeholders, and deliver meaningful results.
Expertise in using data and experimentation to inform product and engineering decisions.
Demonstrated ability to use AI to improve speed and quality in day‑to‑day work for relevant outputs.
Strong track record of critically evaluating and verifying AI-assisted work through testing, review, or other quality controls.