Manager II, Machine Learning Engineering-Content Foundation

Pinterest helps Pinners discover and do what they love. 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 will you be doing:

• 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?

What we’re looking for:

• 7+ years of industry experience, including 2+ years of management experience.
• Masters 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.
• Distributed tooling for ML data pipelines, e.g. Hadoop, Spark.
• 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.

In-Office Requirement Statement:

• We let the type of work you do guide the collaboration style. That means we're not always working in an office, but we continue to gather for key moments of collaboration and connection.
• This role will need to be in the office for in-person collaboration 1-2 times/quarter and can be situated anywhere in the USA.

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