Machine Learning Engineer

Meta•Menlo Park, CA

About The Position

Meta Platforms, Inc. (Meta), formerly known as Facebook Inc., builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps and services like Messenger, Instagram, and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. To apply, click “Apply to Job” online on this web page.

Requirements

Require Master’s degree (or foreign equivalent) in Computer Science, Computer Software, Computer Engineering, Applied Sciences, Mathematics, Physics, or related field and 3 years of experience in the job offered or in a computer-related occupation
Requires 3 years of experience in the following:
Machine learning, deep learning, and statistical modeling
Large-scale data processing and distributed computing (e.g., PyTorch, TensorFlow, Spark)
Python and C++
Experimentation platforms, A/B testing, and data analysis
Problem-solving, communication, and collaboration skills

Responsibilities

Model Development & Deployment: Design, implement, and productionize machine learning models for ad ranking, targeting, and conversion optimization, leveraging large-scale datasets and state-of-the-art algorithms.
Experimentation & Analysis: Lead and analyze A/B tests and offline experiments to evaluate model performance, iterate on features, and drive data-driven decision-making.
System & Pipeline Engineering: Build and maintain robust, scalable ML pipelines and infrastructure, ensuring high reliability and efficiency in model training, evaluation, and deployment.
Collaboration & Cross-Functional Work: Work closely with product managers, data scientists, software engineers, and other stakeholders to define requirements, share insights, and deliver impactful solutions.
Documentation & Knowledge Sharing: Author technical documentation, runbooks, and best practices to support team operations and facilitate onboarding and knowledge transfer.
Mentorship & Leadership: Mentor junior engineers, contribute to code reviews, and help set standards for software quality, testing, and MLOps practices.