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Staff Machine Learning Engineer (Shopping Ads)

Pinterest

Skills

Required

  • ML
  • taking ambiguous problem spaces
  • labeling/review operations
  • metric design
  • regression testing
  • ML-powered

Preferred

  • ML-intensive
  • scalability

Join Pinterest as a Staff Machine Learning Engineer, where you'll drive the future of merchant presence and shopping experiences. You'll lead AI/ML initiatives, partner with cross-functional teams, and establish the technical roadmap for the organization. This high-impact role offers the opportunity to make a direct impact on user trust, relevance, and shopping outcomes.

About Pinterest

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

Responsibilities

  • Conduire des initiatives axées sur les LLM et l'évaluation, en mettant l'accent sur les flux de travail agentiques, la mesure et la rigueur opérationnelle.
  • Diriger la livraison technique de bout en bout pour des initiatives inter-équipes, de la définition du problème à la mise en œuvre et à l'itération.
  • Établir des pratiques d'évaluation et de mesure robustes pour les systèmes assistés par ML et LLM, y compris les ensembles de données d'or, les boucles de révision humaine.
  • Strong systems design skills building data- and ML-intensive systems, with the ability to navigate tradeoffs in performance, reliability, scalability, and cost

Requirements

  • Bachelor’s degree in Computer Science, Engineering, or a related technical field—or equivalent practical experience
  • 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 communication skills and the ability to influence technical direction across teams without directly owning every implementation detail
  • Deep experience with evaluation and measurement: dataset strategy, labeling/review operations, metric design, regression testing, and connecting offline improvements to online outcomes
  • 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
  • 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 experience building and enhancing cross-functional partnerships with other teams and organizations