Staff Machine Learning Engineer, Shopping Ads

About the Role

We’re hiring a Staff Machine Learning Engineer to help drive the future of merchant presence and shopping experiences on Pinterest.

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.

In this role, you’ll lead LLM-first, evaluation-driven initiatives—near-term focused on 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 that improve shopping discovery across Pinterest. 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.

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 in partnership with a Director and cross-functional leads, including defining milestones, sequencing, and quality bars for the domain.
• Build and evolve ML and GenAI systems that improve merchant quality and understanding, with clear downstream impact on 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, and clear launch criteria.
• Design systems with strong attention to quality, cost, latency, reliability, and safety, including guardrails, fallbacks, caching, and observability to support scaled production operations.
• Establish the ML engineering operating model for the org: evaluation standards, launch readiness reviews, monitoring/alerting, and sustainable ownership practices.
• Partner with cross-functional stakeholders across Product, Engineering, Data Science, Design, Trust/Policy/Legal, and ML platform teams.
• Drive experimentation and iteration (A/B tests, holdouts), lead error analysis, and translate learnings into measurable improvements.
• Mentor and raise the bar for technical design, evaluation rigor, and production readiness across the team.
• Help scale the domain by supporting hiring and onboarding over time.

What we’re looking for

• 8+ years of industry experience in ML engineering / applied ML / software engineering, including meaningful time operating as a Staff-level (or equivalent) IC delivering complex production systems.
• Demonstrated ability to lead 0→1 ML/LLM efforts: taking ambiguous problem spaces, defining the approach, and delivering a production system with measurable impact.
• Strong track record shipping ML-powered systems in domains such as recommendation, ranking, retrieval, content understanding, ads relevance, commerce, or adjacent areas.
• Hands-on experience building LLM-powered applications in production (or adjacent GenAI systems), with strong judgment on reliability, failure modes, rollout safety, and practical tradeoffs.
• Deep experience with evaluation and measurement: dataset strategy, labeling/review operations, metric design, regression testing, and connecting offline improvements to online outcomes.
• Strong systems design skills building data- and ML-intensive systems, with the ability to navigate tradeoffs in performance, reliability, scalability, and cost.
• Strong communication skills and the ability to influence technical direction across teams.
• Demonstrated experience building and enhancing cross-functional partnerships.
• Bachelor’s degree in Computer Science, Engineering, or a related technical field—or equivalent practical experience.

In-Office Requirement

This role will need to be in the office for in-person collaboration 1-2 times per month and therefore needs to be in a commutable distance from one of the following offices: San Francisco, CA; Palo Alto, CA; or Seattle, WA.