Facebook Reality Labs (FRL) is a world leader in the design of virtual and augmented reality systems. We are currently seeking innovative researchers with a passion for technology to develop the next generation of Mixed Reality. The focus of this role includes 3D reconstruction, scene understanding, depth estimation, and semantic segmentation. The position requires a combination of education and experience in computer vision, computer graphics, and machine learning.
Research Scientist Mixed Reality Responsibilities
Lead and collaborate on research projects within a globally based team
Conduct applied research to advance the State of the Art in 3D Computer Vision and Machine Learning (Neural Rendering, Depth Estimation, 3D Generative-AI, 3D Reconstruction, Novel View Synthesis, Non-Rigid Tracking).
Collaborate with other researchers, engineers, and 3D artists to prototype hardware and software solutions, experiments and concepts.
Provide leadership to the engineering teams delivering Mixed Reality features into the Presence Platform as developer features available on Metas AR and VR devices.

In this role, you will be researching and applying a range of software, computer vision and machine learning techniques for solving challenges that bridge virtual and real worlds and will impact billions of people. We're addressing a variety of technical challenges in the areas of real-time image processing, 3D graphics, SLAM, scene reconstruction, machine perception, visualisation and human interaction. As well there are opportunities to collaborate with researchers in Facebook Reality Labs (FRL) and Facebook AI Research (FAIR).
Computer Vision Engineer Responsibilities
Design and develop novel computer vision and/or machine learning algorithms in areas such as: real-time scene and object tracking, reconstruction and understanding as well as, segmentation, face tracking, body tracking, key point estimation, depth sensing, generative approaches such as GANs, 3D stereo and volumetric reconstruction, avatars, reconstructions and virtual try-ons.
Develop prototypes for future VR/AR/MR experiences, drive continued development, and integrate robust solutions into product.
Collaborate with cross-functional engineering and research teams from Facebook Reality Labs (FRL) and Facebook AI Research (FAIR)in computer vision, machine learning, and graphics.
Participate in cutting edge research in computer vision that can be applied to AR/VR product development.
Define projects for other engineers to possibly solve and achieve impact based on your direction

Our Reality Labs division explores, develops and delivers cutting-edge technologies that serve as the foundations for the Metaverse and other future Reality Labs products, such as Oculus headsets, future AR glasses and our FB Family of Apps (Messenger, Instagram, WhatsApp). From Visual Localization, SLAM, 3D reconstruction, Context/Semantic Understanding, Mapping, Tracking, and Sensor Fusion, our team is focused on taking new technologies from early concept to the product level while iterating, prototyping, and realizing the human value and new experiences they open up.
Computer Vision Engineer (Leadership) - Reality Labs Responsibilities
Lead the design and development of novel computer vision and/or, machine learning algorithms in areas such as: real-time scene and object tracking, reconstruction and understanding, as well as, segmentation, face tracking, body tracking, key point estimation, depth sensing, generative approaches such as GANs, 3D stereo and volumetric reconstruction, avatars, reconstructions and virtual try-ons
Play a critical role in the definition and execution of long-term roadmaps in partnership and cross functional organizations in computer vision, machine learning, graphics, sensors, optics, and silicon
Lead and collaborate with multidisciplinary engineering and research teams to develop technologies from early exploration and incubation to production
Be a go-to person to escalate the most complex online / production performance and evaluation issues that require an in depth knowledge
Develop prototypes for future VR/AR/MR experiences, drive continued development, and integrate robust solutions into products
Participate in cutting edge research in computer vision that can be applied to AR/VR product development
Define projects for other engineers to possibly solve and achieve impact based on your direction

We are looking for researchers and applied scientists to join the Central Applied Science team. Central Applied Science is an interdisciplinary team of quantitative scientists that aims to deliver research and innovation that fundamentally contribute to Meta's success. By applying your expertise in quantitative methods, you will be empowered to drive impact across a range of products, infrastructure and company operations. Individuals in this role are expected to have expertise and publications within research areas including artificial intelligence, machine learning, statistics, causal inference and experimentation. The ideal candidate will have a passion for building data-driven products and forming research frameworks to solve challenging, real-world problems.
Research Scientist, Central Applied Science (PhD) Responsibilities
Work with vast amounts of data, generate research questions that push the state-of-the-art, and build data-driven products.
Develop novel quantitative methods on top of Meta's unparalleled data infrastructure.
Learn new tools, systems, and programming languages quickly as required by the particular project you are working on
Work towards long-term ambitious research goals, while identifying intermediate milestones.
Communicate best practices in quantitative analysis to partners.
Work both independently and collaboratively with other scientists, engineers, UX researchers, and product managers to accomplish complex tasks that deliver demonstrable value.
Publish research results in top-tier conferneces and at leading journals
Actively identify new opportunities within Meta's long term roadmap for data science contributions.

Be a part of a fast moving product team with a great track record of landing significant impact. The position will involve collaborating closely with product teams to capture our users intentions and applying tech skills to build end to end ML solutions that will drive performance and help better personalizing our ads experiences on top of Meta's state of the art ad systems. You will bring the ability to own the whole ML life cycle, define projects and drive excellence across teams. You will work alongside the worlds leading engineers and researchers to solve some of the most exciting and massive social data and prediction problems that exist on the web.
Machine Learning Engineer - Core Ads Ecommerce Responsibilities
Partner with product teams and other engineers to solve Shops Ads business problems through ML
Play a critical role in setting the direction and goals for a sizable team, in terms of project impact, ML system design, and ML excellence
Re-evaluate the tradeoffs of already shipped features/ML systems, and you are able to drive large efforts across multiple teams to reduce technical debt, designing from first principles when appropriate
Leading a team from a technical perspective to develop ML best practices and influence engineering culture
Be a go-to person to escalate the most complex online / production performance and evaluation issues, that require an in depth knowledge of how the machine learning system interacts with systems around it
Suggest, collect and synthesize requirements and create effective feature roadmap
Code deliverables that will tie the ML solutions you develop to our ad delivery stack

We are using a variety of ML and stats models and algorithms, from classic supervised learning, to anomaly detection, unsupervised learning and semi supervised learning, to detect and mitigate ongoing fraud campaigns.

In our solutions we are using Graph Neural Networks, Ensembles, Boosting, Regression models, Time Series data, Causal Inference and more.
Software Engineer, Machine Learning (Tel Aviv) Responsibilities
Leading projects or small teams of people to help them unblock, advocating for ML excellence.
Develop highly scalable classifiers and tools leveraging machine learning, data, and rules based models
Suggest, collect and synthesize requirements and create effective feature roadmaps
Code deliverables in tandem with the engineering team

Our Tel-Aviv team develops machine-learning models that serve billions of buyers daily. To do so, we develop state-of-the-art recommendation and ranking systems. We work closely with domain experts to design features and embedding spaces best fitted for eCommerce. Finally, we use Meta's cutting-edge generative models to personalize the experience for our buyers.
Our team also builds AI capabilities to enhance Metas product catalogs to improve ads performance. Catalog-powered ads account for a significant portion of Metas revenue, with unlocking deep semantic understanding of our products being a key strategic lever for revenue growth.
If you're passionate about ML at scale and want to work with an amazing group of people - look no further - come and join us!
Software Engineer, Machine Learning Responsibilities
Leading projects or small teams of people to help them unblock, advocating for ML excellence
Adapt standard machine learning methods to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU)
Develop highly scalable classifiers and tools leveraging machine learning, data regression, and rules based models
Suggest, collect and synthesize requirements and create effective feature roadmaps
Code deliverables in tandem with the engineering team

The Risk team in Financial Integrity is responsible for mitigating all fraudulent financial activity that is done in Meta products in order to drive trust and integrity for all people and businesses using Metas financial ecosystem, while accelerating growth.

Fraud prevention is one of Metas leading investment areas as part of the financial activity expansion in our portfolio of products. As part of this, we are supporting highly scalable systems that are able to support tens of billions of transactions per year.

The team is composed of software engineers, machine learning engineers, data scientists which are mostly seniors in developing state of the art backend systems, supervised and unsupervised machine learning models and our focus is to continue to lead industry standards in the FinTech space.
Machine Learning Engineer - Financial Integrity, Risk Responsibilities
Have a leading position in setting the direction and goals for the ML pillar, in terms of project impact, ML system design, and ML excellence.
Creating and fostering long term vision on building first class, state of the art fraud enforcement solutions
Owning the entire development cycle, from ideation to realization across the stack. Designing and developing machine learning models using supervised, unsupervised, and deep learning techniques.
Take part in the overall team engineering efforts and contribute with hands-on work
Working with software engineers to integrate machine learning models into applications and systems
Collaborating with data scientists to identify and select appropriate machine learning algorithms and techniques

In Shop Ads, we personalize sellers' shops to delight our buyers' and satisfy their needs. From the products to the text and media that complement them, the layout that makes it most accessible - every decision has the buyer in mind.
Our Tel-Aviv team develops machine-learning models that serve billions of buyers daily. To do so, we develop state-of-the-art recommendation and ranking systems. We work closely with domain experts to design features and embedding spaces best fitted for eCommerce. Finally, we use Meta's cutting-edge generative models to personalize the experience for our buyers.
Our team also builds AI capabilities to enhance Metas product catalogs to improve ads performance. Catalog-powered ads account for a significant portion of Metas revenue, with unlocking deep semantic understanding of our products being a key strategic lever for revenue growth.
If you're passionate about ML at scale and want to work with an amazing group of people - look no further - come and join us!
Software Engineer, Machine Learning Responsibilities
Play a role in setting the direction and goals for the ML pillar, in terms of project impact, ML system design, and ML excellence.
Owning the entire development cycle, from ideation to realization across the stack. Designing and developing machine learning models using supervised, unsupervised, and deep learning techniques.
Implementing and testing machine learning models in a production environment
Take part in the overall team engineering efforts and contribute with hands-on work
Working with software engineers to integrate machine learning models into applications and systems
Collaborating with data scientists to identify and select appropriate machine learning algorithms and techniques

We are seeking a talented MLOps Engineer to join our innovative team. In this pivotal role, you'll be at the heart of our operations, bridging development, machine learning, and large language models (LLMs) to ensure seamless, efficient, and scalable deployment of our AI-driven solutions. If you're passionate about cutting-edge technology, excel in a dynamic environment, and are keen to make a significant impact, we're eager to connect with you!

Key Responsibilities
Collaboration: Work closely with ML engineers, data scientists, and software developers to integrate ML/LLM models into production.
Automation and Process Definition: Define work processes and implement automation pipelines to streamline ML/LLM development and deployment, enhancing productivity and efficiency.
Deployment and Scaling: Deploy, monitor, and scale machine learning models and large language model applications across various environments.
Monitoring and Maintenance: Implement monitoring solutions for performance and health of ML/LLM models, ensuring high availability and reliability.
Optimization: Continuously optimize ML pipelines for improved performance, scalability, and cost-efficiency. Security and Compliance: Ensure adherence to data privacy, security protocols, and compliance standards in ML/LLM operations.
Innovation: Keep abreast of the latest developments in DevOps, ML, and LLM technologies to drive continuous improvement and innovation.