Engineering Manager

Pinterest helps Pinners discover and do what they love. The Content Foundation ML team builds content understanding capabilities that support Discovery, Search, Growth, and Ads.

• Lead, mentor, and grow a team of engineers and applied scientists developing foundational content-understanding signals, infrastructure, and systems for Pinterest products across Discovery, Search, Growth, and Ads.
• Set the long-term technical vision for semantic signal generation, reusable foundational layers, and scalable model-powered infrastructure. Partner with product, data science, and downstream engineering teams to drive impact.
• Manage project execution and stakeholder communication, including roadmap planning, technical decisions, risk mitigation, and progress updates.
• Guide the team in using VLM and LLM technologies to understand Pinterest content and generate high-quality signals with strong precision, recall, and coverage across locales.
• Build reusable foundational layers such as captions, summaries, and semantic descriptors for efficient downstream use and low infrastructure cost.
• Improve inference throughput, freshness, and serving efficiency for content signals at scale.
• Measure and improve the impact of content signals on relevance, personalization, engagement, and ads performance.
• Build monitoring, evaluation, and debugging systems to ensure signal quality, accessibility, latency, and freshness.

• 7+ years of industry experience, including 2+ years of management experience.
• Master's degree in computer science, computer engineering, or equivalent work experience.
• Publications at top machine learning and data mining conferences, such as NeurIPS, ICML, KDD, or SIGIR.
• Experience with large-scale distributed training and inference of LLM and VLM models; distributed tooling for ML data pipelines such as Hadoop and Spark; and leading ambiguous ML projects to build, apply, and improve GenAI models for content understanding and recommendation systems.
• Proven technical leadership across ML and data systems, including setting direction for multi-quarter roadmaps spanning modeling, data pipelines, and productionization, and aligning stakeholders on priorities, trade-offs, and execution plans.
• Excellent cross-functional communication and collaboration, building partnerships with product, data science, and partner ML teams and driving consensus with senior stakeholders.

• Can be situated anywhere in the USA; in-office collaboration is required 1–2 times per quarter.
• Not eligible for relocation assistance.
• US applicants only. Position is also eligible for equity.
• Base salary range: $222,716–$389,753 USD annually.