Machine learning (ML) can be thought of as a way to recognize and draw conclusions from connections among data. Machine learning allows speech recognition systems to caption videos on Facebook, making them more accessible. It powers the translation of more than 2 billion stories every day, so people can connect in any language. It makes connections between people and local businesses. It allows a panoramic photo to be quickly transformed into a 360-degree immersive experience. Machine learning engineers and researchers at Facebook, Messenger, Instagram, WhatsApp and Meta Quest work together to solve technical challenges like these billions of times every day.

Team members discuss the complexity of the problems we need to solve as we continue to identify and remove misleading stories from Facebook Feed. The solutions require a deep commitment to changing Meta for the better; empowering members of our global community to let us know when they see false info; and machine learning at an enormous scale.

Several technologies were developed and deployed in the past year to optimize the way people capture, create and share 360-degree content. We also revolutionized how we store high-resolution media, using deep neural nets to automatically reorient 360 photos.

Language translation is one of the ways we can give people the power to build community and bring the world closer together. It can help people connect with family members who live overseas, or better understand the perspective of someone who speaks a different language. We use machine translation to translate text in posts and comments automatically, in order to break language barriers and allow people around the world to communicate with each other.

The ability to place and lock in digital objects relative to real-world objects is known as simultaneous localization and mapping (SLAM), and it's an ongoing challenge in computer vision and robotics research. Our Applied Machine Learning (AML) team used initial work done at Meta Quest in their computer vision group to build and deploy SLAM while solving the need for device-tailored algorithms, small-as-possible binary size and a believable experience.

Read more about machine learning on research.facebook.com and code.facebook.com

In offices around the world, our machine learning researchers and engineers tackle complex challenges at an unprecedented scale. Learn more about their diverse backgrounds, the paths that led them to machine learning at Meta and the problems we're solving together.

Kristen, New York

Damien, London

As a research scientist on the Ads Ranking team, Damien focuses on causal inference and large-scale machine learning problems to make advertising on Meta technologies more relevant to people. Reflecting our belief that a manager's job is to encourage team members to use their strengths, Damien says: “One of the reasons I love working at Meta is because of how management is about supporting and growing our engineers to make them as successful as possible.”

Daniel, Menlo Park

Daniel manages several teams of ML researchers and engineers in Menlo Park and Seattle within the integrity domain. They apply machine learning at a huge scale to help the Meta technologies understand text, image, video and audio, and show only high-quality content to people. Daniel explains the team's goals and impact: “We directly contribute to keeping user experiences safe and building meaningful connections between people and businesses on Meta technologies.”

A hub for talented engineers, Seattle is a place where you can build and ship new products to help billions of people around the world. Our machine learning engineers work in small teams with researchers and designers to build new products and features that give people the power to build community together. Teams in Seattle include Messenger, Videos, Mobile Performance, Mobile App Ads and Mobile Platform.

Like the city itself, the Meta office in New York is full of opportunities to move fast and be bold. We work together in small teams and constantly innovate to build new products for millions of people around the world. Instagram teams in New York include Feed and Stories ranking, Search and Explore ranking and ML Infrastructure. We also have Facebook Feed data science and ranking teams, Local Search, Meta AI Research Engineering, Applied Machine Learning (AML) and more opportunities. One of the offices is located in the East Village of Manhattan, close to some of the best restaurants, bars, theaters and concert venues in the city.

Our Boston office feels like a startup, with the benefits and support of a global organization. Machine learning researchers and engineers in Boston work on small teams including AI Infrastructure and GeoAPI. We're passionate about building tools and features that bring people and communities together. Team members drive key technical decisions on their projects and help shape the culture of Boston.

A global hub of culture and innovation, London is an ideal place for machine learning engineers to make an impact. Our teams develop machine learning models to deliver high quality results for people through Feed, ads, search and more. One team supports our global community by developing artificial intelligence (AI) systems to detect malicious content. Another team applies machine learning to help people collaborate through business products like Workplace.

Our global headquarters is located in the heart of Silicon Valley, where we’re focused on a bold mission to bring the world closer together and give people the power to build community. Machine learning researchers and engineers work on small teams here that include AI Research, Applied Machine Learning, product teams and AI Infrastructure. Some of the high-impact teams include Integrity, Messenger and Instagram, to name a few.

The Tel Aviv office is expanding and improving connectivity around the globe. From designing human-centered experiences to building groundbreaking technology, the Tel Aviv office has a variety of opportunities to solve the complex problems that impact billions of people every day.

Click here to view all machine learning opportunities at Meta. Learn more about Tech at Meta here.