Manager II, Machine Learning Engineering-Content Foundation

Pinterest is a platform that helps people discover and act on creative ideas. The Manager II, Machine Learning Engineering-Content Foundation will lead a team developing scalable content-understanding signals, infrastructure, and systems using machine learning and large multimodal models. The role will set technical direction, manage execution, and partner with product, data science, and engineering teams to drive impact across Discovery, Search, Growth, and Ads.

Responsibilities:

• Lead, mentor and grow a team of engineers and applied scientists in developing foundational content understanding signals, infrastructure and systems that are integral to key Pinterest products across Discovery, Search, Growth, and Ads
• 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 these signals drive meaningful impact across Pinterest
• Manage project execution and stakeholder communication, including roadmap planning, technical decision-making, risk mitigation, and progress updates to achieve business goals
• Help the team solve difficult technical challenges such as:
• How to leverage the latest VLM and LLM technologies to understand the full semantic richness of Pinterest content and generate high-quality signals with strong precision, recall and coverage across multiple locales?
• How to build reusable foundational layers such as captions, summaries and semantic descriptors that can power multiple downstream use cases efficiently and at low infra cost?
• How to improve inference throughput, freshness and serving efficiency so that content understanding signals can keep up with millions of new Pins and evolving content every day?
• How to measure and improve the downstream impact of content understanding signals on relevance, personalization, engagement and ads performance across different surfaces?
• How to build robust monitoring, evaluation and debugging systems that ensure signal quality, accessibility, latency and freshness at scale?

Requirements:

• 7+ years of industry experience, including 2+ years of management experience
• Masters degree in computer science, computer engineering, or equivalent work experience
• 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 with 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 that span modeling, data pipelines, and productionization, and aligning stakeholders on priorities, trade-offs, and execution plans
• Excellent cross-functional communication and collaboration skills, building strong partnerships with product, data science, and partner ML teams to clarify ambiguous problem spaces, co-create solutions, and drive consensus with senior stakeholders
• Publication at top machine learning and data mining conferences (e.g., NeurIPS, ICML, KDD, SIGIR)