Research Engineer - Core ML - FB Search Team

Meta • Menlo Park, CA

35d

About The Position

Meta is seeking a Research Engineer to join our Meta Recommendation Systems (MRS) Search AI team. We are part of a multi-year investment in a next-generation search experience across Instagram & Facebook that is complementary to our recommendation strategy. We are building search systems on a foundation of state-of-the-art AI technology. As the Core Modeling team, we focus on finding the most relevant, personalized content to match content query intent of users for Facebook. Among trillions of Facebook contents, we drive industry-leading technologies to build models that increases Meta content search topline metrics. We're looking for a Research Engineer to help us keep innovating the models and push the search experience to the next level with SoTa LLM technologies.

Requirements

• Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta
• Experience in machine learning, deep learning, and/or natural language processing
• Experience with developing machine learning models at scale from inception to business impact
• Programming experience in Python and hands-on experience with frameworks such as PyTorch
• Exposure to architectural patterns of large scale software applications
• Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment

Nice To Haves

• Direct experience in Search / Recommendations
• First author publications at peer-reviewed AI conferences (e.g., NeurIPS, CVPR, ICML, ICLR, ICCV, and ACL)
• Master's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• A PhD in AI, computer science, data science, or related technical fields

Responsibilities

• Design methods, tools, and infrastructure to push forward the state of the art in search and ranking models
• Define goals informed by practical engineering concerns
• Contribute to experiments, including designing experimental details, developing reusable code, running evaluations, and organizing results
• Adapt standard machine learning methods to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU)
• Work with a large and globally distributed team
• Contribute to publications and open-sourcing efforts

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level