Staff Machine Learning Engineer, Content Visual AI

Pinterest•Seattle, WA

4d•$189,308 - $389,753•Hybrid

About The Position

The User and Content Foundations org is responsible for deeply understanding our content, users and their interactions by generating high-quality, reusable signals and insights that power relevance and personalization across the platform. We work at the intersection of distributed machine learning, large multimodal models, data infrastructure, and product adoption to build foundational capabilities that support our partners, including Discovery, Search, Growth, and Ads. We are looking for an experienced Staff Machine Learning Engineer / Technical Lead 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. In this role, you will lead ambiguous, high-impact scientific and engineering efforts that create reusable representations, models, and signals. You’ll work across research, machine learning, infrastructure, and engineering domains to turn advances in VLMs, LLMs, multimodal learning and sequence modeling into impact. This role is ideal for a deeply technical leader who combines strong ML judgment with product intuition. Expertise in visual representation learning and content understanding is especially valuable; experience with user understanding, recommender systems and LLMs is also highly relevant.

Requirements

Minimum 7 years of industry experience, including 2+ years of tech leading teams
MS or PhD degree in computer science, machine learning, or equivalent industry experience.
Publications at top machine learning, multimodal and/or data mining conferences (e.g., NeurIPS, ICML, CVPR, ECCV, ACL, KDD, SIGIR, etc.) or open-source ML experience
Hands-on experience with large-scale distributed training of generative models (LLMs, VLMs, sequence models).
Hands-on experience with distributed tooling for ML data pipelines (e.g. Spark, Hive, MapReduce)
Hands-on experience leading ambiguous ML and research efforts in building, applying and improving GenAI models for content understanding, search and recommendation systems.
Technical leadership and experience in setting direction for 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

Nice To Haves

Expertise in visual representation learning and content understanding is especially valuable
Experience with user understanding, recommender systems and LLMs is also highly relevant.

Responsibilities

Set the technical direction and multi-quarter strategy for foundational ML capabilities across visual representation learning, multimodal content understanding, and LLM-based personalization.
Lead high-impact scientific and technical initiatives from problem definition and research strategy through experimentation, productionization, and adoption.
Build reusable embeddings, representations, semantic signals, and ML infrastructure while advancing techniques such as VLMs, LLMs, sequence modeling, self-supervised learning, retrieval, and distillation.
Partner with Product, Data Science, Applied Science, and Engineering to translate foundational capabilities into product outcomes and influence cross-functional roadmaps and investments.
Establish rigorous practices for modeling, data pipelines, distributed training, inference, evaluation, experimentation, and operational excellence while mentoring engineers and scientists.