Staff Machine Learning Engineer (Content Visual AI)
Posted Updated
Description
- 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
- 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
- Expertise in visual representation learning and content understanding is especially valuable; experience with user understanding, recommender systems and LLMs is also highly relevant
- This role is ideal for a deeply technical leader who combines strong ML judgment with product intuition
- Hands-on experience with large-scale distributed training of generative models (LLMs, VLMs, sequence models)
- 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 leading ambiguous ML and research efforts in building, applying and improving GenAI models for content understanding, search and recommendation systems
- Minimum 7 years of industry experience, including 2+ years of tech leading teams
- 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
- Hands-on experience with distributed tooling for ML data pipelines (e.g. Spark, Hive, MapReduce)