Manager II, Machine Learning Engineering-Content Foundation

About the Role

The Content Foundation ML team is responsible for understanding the semantic meaning of Pinterest content and generating high-quality, reusable signals and insights that power relevance and personalization across the platform. We work at the intersection of machine learning, large multimodal models, data infrastructure, and product adoption to build foundational content understanding capabilities that support Discovery, Search, Growth, and Ads.

We are looking for an experienced Engineering Manager who can drive the team’s technical direction, lead the team in building scalable content understanding systems, and make an impact on core Pinterest user and business outcomes.

What you'll do

• Lead, mentor, and grow a team of engineers and applied scientists developing foundational content understanding signals, infrastructure, and systems.
• Provide thought leadership in content understanding and machine learning by setting a long-term technical vision for semantic signal generation, reusable foundational layers, and scalable model-powered infrastructure.
• Partner closely with product, data science, and downstream engineering teams to ensure signals drive meaningful impact across Pinterest.
• Manage project execution and stakeholder communication, including roadmap planning, technical decision-making, risk mitigation, and progress updates.
• Guide the team in solving complex challenges around VLM and LLM technologies, reusable foundational layers (captions, summaries, semantic descriptors), inference throughput/efficiency, downstream impact measurement, and robust monitoring/evaluation.

What we're looking for

• 7+ years of industry experience, including 2+ years of management experience.
• Master's degree in computer science, computer engineering, or equivalent work experience.
• Publication at top machine learning and data mining conferences (e.g., NeurIPS, ICML, KDD, SIGIR).
• Experience with large-scale distributed training and inference of LLM and VLM models.
• Experience with distributed tooling for ML data pipelines (e.g., Hadoop, Spark).
• Experience leading ambiguous ML projects to build, apply, and improve GenAI models for content understanding and recommendation systems.
• Proven technical leadership across both ML and data systems, with experience setting direction for multi-quarter roadmaps spanning modeling, data pipelines, and productionization.
• Excellent cross-functional communication and collaboration skills.

Working Model

• This role can be situated anywhere in the USA with in-person collaboration required in the office 1-2 times per quarter.