Senior ML Engineer: Personalization & Recommender Systems

Pinterest Engineering; San Francisco (CA); On-site; USD 190,000 - 332,000; Full time

Job summary

Pinterest is seeking an Ads ML Engineer in the United States to evolve the machine learning technology stack within Ads. You will build advanced ML models to personalize the Pinterest experience, collaborating with cross-functional teams and leveraging data-driven methods to improve candidate retrieval.

The role emphasizes rapid experimentation, staying current with recommendation systems trends, and applying LLMs to enhance content understanding, with a base salary range and US-based

Qualifications

• 2+ years of industry experience applying ML methods (e.g., user modeling, personalization, recommender systems, search, ranking, NLP, RL, graph representation learning).
• Degree in computer science, statistics, or related field; or equivalent experience.
• End-to-end hands-on experience with data pipelines, large-scale ML systems and big data technologies (e.g., Hadoop/Spark).

Responsibilities

• Build cutting-edge technology using DL/ML to personalize Pinterest.
• Partner with teams across Pinterest to improve ML models for product surfaces (Homefeed, Ads, Growth, Shopping, Search).
• Use data-driven methods and leverage data to improve candidates retrieval.
• Work in high-impact environment with quick experimentation and product launches.
• Keep up with industry trends in recommendation systems.
• Leverage LLMs to enhance content understanding.

Skills

Machine Learning

Recommender systems

Deep learning

Natural language processing

Big data

Python

SQL

LLMs

Education

BS in Computer Science or related field

Tools

Cursor

Copilot/Codex

SQL

Job description

Pinterest is seeking an Ads ML Engineer in the United States to evolve the machine learning technology stack within Ads. You will build advanced ML models to personalize the Pinterest experience, collaborating with cross-functional teams and leveraging data-driven methods to improve candidate retrieval.

The role emphasizes rapid experimentation, staying current with recommendation systems trends, and applying LLMs to enhance content understanding, with a base salary range and US-based