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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.

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

Reality Labs at Meta is building products that make it easier for people to connect with the ones they love most, enjoy top-notch, wire-free VR, and push the future of computing platforms. We are a team of world-class experts developing and shipping products at the intersection of hardware, software and content.

As Computer Vision Software Engineer on the Reality Labs team at Meta, you can help build new, innovative hardware and software that radically redefine the way people work, play and connect. What we build today could one day be the norm. So to be here today is to truly be at the heart of change and the frontier of what's to come. We're the people helping to define the metaverse. We may not have all the answers. But together, we're getting closer.

Meta is seeking AI Software Engineers to join our Research & Development teams. The ideal candidate will have industry experience working on computer vision related topics. The position will involve taking these skills and applying them to solve for some of the most crucial & exciting problems that exist on the web. We are hiring in multiple locations.
Software Engineer, Computer Vision - Reality Labs Responsibilities
Apply relevant AI and machine learning techniques to build intelligent rich visual systems that improve Meta's products and experiences
Assist in goal setting related to project impact, AI system design, and ML excellence
Drive large efforts across multiple teams
Mentor other AI Engineers & improve the quality of AI work in the broader team
Develop custom/novel architectures, define use cases, and develop methodology & benchmarks to evaluate different approaches
Apply in-depth knowledge of how the machine learning system interacts with the other systems around it

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
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

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

The Cloud Guard research team is looking for a curious and highly motivated Cloud Security Researcher who will translate the research to direct security value for the company and product.

As a Cloud Security Researcher in the Cloud Guard area, you'll conduct ongoing research on different cloud infrastructures and services, detect and replicate different attacks, and publish technical blog posts.

Key Responsibilities
Initiate and conduct Cloud Research Initiatives throughout the realm of cloud cyber security.

Conduct cutting-edge research on all different cloud vendors and become an authoritative source of knowledge for the team.

Collaborate with cross-functional teams: Work closely with various teams to drive cloud detections in the product.

Publish intensive, technical blog posts on attack discovery and deep insights.

Similarweb is the leading digital intelligence platform used by over 3500 global customers. Our wide range of solutions power the digital strategies of companies like Google, eBay, and Adidas.

We help our customers succeed in todays digital world by giving them access to data-driven insights, competitive benchmarks, strategic analysis, and more. In 2021, we went public on the New York Stock Exchange, and we havent stopped growing since!

We are looking for a talented and dedicated ML & Big Data Analyst who will be a part of the Core Data group.

As part of this role, you will help us process and streamline rich and complicated datasets. Will explore and validate new data sources. Be part of the data science and data engineering force creating new features for our solution.

What does the day-to-day of a ML & Big Data Analyst at Similarweb look like:

Apply your expertise in quantitative analysis and data mining to turn data into insights.
Conduct research and develop tools that will ensure the quality of Similarwebs data and algorithms.
Utilize your deep understanding of Similarwebs data in order to evaluate and improve novel algorithms produced by the data scientists in your team.
Partner with Big Data Engineers to establish an infrastructure to scale solutions.

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.