Staff Machine Learning Engineer, Shopping Ads
Pinterest · San Francisco, CA, US; Palo Alto, CA, US; Seattle, WA, US
About this role
**About Pinterest** Millions of people around the world come to Pinterest to find creative ideas, dream about new possibilities, and plan for memories that will last a lifetime. At Pinterest, AI is a powerful partner that augments creativity and amplifies impact. We’re hiring a **Staff Machine Learning Engineer (Shopping Ads)** to help drive the future of **merchant presence and shopping experiences** on Pinterest. --- **Role Overview** This role sits on the **Merchant** team and focuses on building **AI/ML systems (including LLMs)** that identify, understand, and surface **relevant, high-quality merchants** across segments—so Pinners can discover new brands with greater confidence and consideration, and merchants can reach new, diverse audiences. You’ll lead **LLM-first, evaluation-driven initiatives** (near-term: agentic workflows, measurement, and operational rigor) that strengthen **Merchant Integrity** and **Business Integrity**. Longer term, you’ll help advance core relevance capabilities such as **merchant/brand affinity modeling** and related signals to improve shopping discovery across Pinterest. You’ll partner closely with **Product Managers, Engineering Managers, Data Science, Design, and platform teams** to take systems from early prototypes to reliable, scaled production. You will also serve as the **technical lead for ML** in this space—reporting to a Director and acting as the **first ML Engineering hire** in this org—helping define the technical roadmap, establish engineering standards, and lay the foundation for scaling the domain and team over time. --- **What you’ll do** - Own end-to-end technical delivery for cross-team initiatives—from problem framing and technical strategy through architecture, implementation, rollout, monitoring, and iteration. - Set technical direction and execution plans with a Director and cross-functional leads (milestones, sequencing, and quality bars). - Build and evolve ML + GenAI systems to improve merchant quality and understanding, including: - merchant content enrichment - attribute extraction/normalization - entity resolution - merchant/brand quality signals - policy-aware transformations - and connect these to downstream retrieval, ranking, and shopping surfaces - Establish robust evaluation and measurement practices across ML + LLM-assisted systems, including: - golden datasets - human-in-the-loop review loops - automated regression testing - offline/online metric alignment - clear go/no-go launch criteria for quality, safety, and performance - Design systems with strong attention to **quality, cost, latency, reliability, and safety** (guardrails, fallbacks, caching, observability). - Establish the ML engineering operating model (where applicable): evaluation standards, launch readiness reviews, monitoring/alerting, and sustainable ownership. - Partner across Product, Engineering, Data Science, Design, Trust/Policy/Legal, and ML platform teams to align on goals/constraints and turn ambiguous needs into concrete ML deliverables. - Drive experimentation and iteration (A/B tests, holdouts), lead error analysis, and translate learnings into measurable improvements to user trust and shopping outcomes. - Mentor and raise the bar for technical design, evaluation rigor, and production readiness. - Help scale the domain by supporting hiring and onboarding over time (interview loops, onboarding plans, technical mentorship). --- **What we’re looking for** - **8+
Listing freshness
CronJobs last confirmed this listing 9h ago. If its source stops confirming the opening for seven days, this page is removed from active inventory.