Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.

At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.

The ML & Media Infrastructure team enables fast, safe, and efficient delivery of AI/ML solutions across Ads and Core critical products. We build unified data, training, feature, and inference infrastructure; this role will set technical direction across model training and serving, with a focus on GPU efficiency and large-scale ranking systems. 

The ML & Media Infrastructure team also owns the critical platforms for content acquisition, media ingestion and media signal processing. The team closely collaborates with multiple partner teams bringing the most inspiration content to Pinterest to enhance the content foundation of all products, adopting LLM/VLM in a near real-time system to understand content and build fundamental signals for LLM innovations, and standing in the frontier to meet GenAI compliance requirements.

What you’ll do

• Set the technical vision and roadmap for model training and serving, with reusable interfaces to data and feature infrastructure.
• Lead architectures for distributed training, fine-tuning, distillation, evaluation, and high-scale CPU/GPU inference.
• Lead architectures for content acquisition, media ingestion and signal processing.
• Improve efficiency across data loading, distributed execution, GPU kernels and memory, compilation, quantization, scheduling, and capacity.
• Build reliable, observable platforms with strong quality guarantees and training/serving consistency.
• Partner with Ads and Core AI/ML teams to productionize features and models safely at Pinterest scale.
• Drive cross-organizational architecture decisions, migrations, and operational standards; mentor senior engineers and raise the engineering bar.
• Partner closely with Content, Shopping catalog and GenAI teams to improve Pinterest content ecosystem.
• Use AI-assisted development and analysis to accelerate prototyping, performance diagnosis, and validation while maintaining rigorous correctness and data safeguards.

What we’re looking for

• A track record of setting technical strategy and delivering company-wide infrastructure initiatives in ambiguous environments.
• Deep expertise in distributed ML systems, including production experience with both large-scale training and online inference.
• Strong GPU performance knowledge, such as profiling, distributed execution, kernel and memory optimization, compilation, or quantization.
• Experience with AI/ML modeling, recommender systems, Ads ranking, retrieval, feature platforms, or similarly demanding ML workloads.
• Experience with content acquisition e.g. crawler, content understanding, NRT data processing.
• Experience with search and recommendation system, ads serving system.
• Strong systems programming and design skills in C++, Java, or Python.
• High ownership and sound judgment in reliability, security, cost, and operational excellence.
• Demonstrated ability to use AI to improve speed and critically evaluate AI-assisted work, with accountability for correctness, quality, and sensitive data.
• Bachelor’s degree in Computer Science, Engineering, a related field, or equivalent experience.

Relocation Statement

• This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.

In-Office Requirement Statement

• We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.
• This role will need to be in the office for in-person collaboration 1–2 times per quarter and therefore needs to be within a commutable distance of our Palo Alto, CA or San Francisco, CA office.

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