Staff Machine Learning Engineer, Shopping Ads

Pinterest•Palo Alto, CA

4d•$222,716 - $389,753•Hybrid

About The Position

Pinterest is looking for a Staff Machine Learning Engineer to drive the future of merchant presence and shopping experiences on the platform. This role focuses on building AI/ML systems, including LLMs, to identify, understand, and surface relevant, high-quality merchants. The goal is to enable Pinners to discover new brands with confidence and help merchants reach new audiences. The role involves leading LLM-first, evaluation-driven initiatives, focusing on agentic workflows, measurement, and operational rigor for Merchant and Business Integrity. Longer-term, the role will advance core relevance capabilities like merchant/brand affinity modeling to improve shopping discovery. The engineer will partner with Product Managers, Engineering Managers, Data Science, Design, and platform teams to move systems from prototypes to production. This position serves as the technical lead for ML in this space, reporting to a Director, and will define the technical roadmap, establish engineering standards, and build the foundation for scaling the domain and team. It's a high-agency, high-impact role influencing user trust, relevance, and shopping outcomes across Pinterest surfaces.

Requirements

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 with clear product impact.
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 without directly owning every implementation detail.
Demonstrated experience building and enhancing cross-functional partnerships with other teams and organizations.
Bachelor’s degree in Computer Science, Engineering, or a related technical field—or equivalent practical experience.

Responsibilities

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 (e.g., merchant content enrichment, attribute extraction/normalization, entity resolution, merchant/brand quality signals, and policy-aware transformations), 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 go/no-go launch criteria for quality, safety, and performance.
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 (where applicable): evaluation standards, launch readiness reviews, monitoring/alerting, and sustainable ownership practices to keep quality high as the roadmap scales.
Partner with cross-functional stakeholders across Product, Engineering, Data Science, Design, Trust/Policy/Legal, and ML platform teams to align on goals, constraints, and rollout plans—and to 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 across the team—enabling faster, safer iteration with AI/ML tooling and best practices.
Help scale the domain by supporting hiring and onboarding over time (e.g., interview loops, onboarding plans, technical mentorship), as we build out ML engineering capacity.