Sr. Machine Learning Engineer

Pinterest Job Advertisements•San Francisco, CA

2d•$246,916 - $332,012•Remote

About The Position

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. Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible.

Requirements

PhD (or foreign equivalent) in Computer Science, Software Engineering, or closely related quantitative discipline
two (2) years of experience in the job offered or at least one year of experience in the job offered or in a software engineer/machine learning engineer-related position.
At least one year of experience using Java for developing scalable, high-performance backend services that power real-time recommendation pipelines
At least one year of experience experimenting and improving machine learning models
At least one year of experience with development and optimization of large scale recommendation algorithms
At least one year of experience applying Natural Language Processing to recommendation systems.
Python
Solving analytical problems using quantitative approaches
Adapting standard machine learning methods to best enterprise modern parallel environments: distributed clusters, multicore SMP, or GPU
Algorithms, data structure
Signal processing
Data mining
Gathering, manipulating, or analyzing complex, high-volume, high-dimensionality data from varying sources.

Responsibilities

Build cutting edge technology using the latest advances in deep learning and machine learning to personalize Pinterest.
Partner closely with teams across Pinterest to experiment and improve Machine Learning models for various product surfaces (Homefeed, Ads, Growth, Shopping, and Search), while gaining knowledge of how ML works in different areas.
Use data driven methods and leverage the unique properties of our data to improve candidates retrieval.
Work in a high-impact environment with quick experimentation and product launches.
Keeping up with industry trends in recommendation systems.