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Adaptive cloud is a new and exciting domain, bridging the gaps between cloud environment and edge devices, so that our customers can run their workloads where it best suits them. This is an opportunity to influence the security of this domain, covering cloud security, OS environments, deployment challenges and end to end research.

The Edge & Platform Security Fundamentals (EPSF) org ensures we ship the world's most secure operating systems, cloud platforms, and edge devices. We conduct research into the highest priority attack surfaces and scenarios, including into Microsoft strategic investments such as Adaptive Cloud, AI, and next generation OS. Our research teams include leading researchers in this domain, finding and fixing critical issues, and developing techniques and tools that help us scale the security research.

Being part of EPSF IL, you will build a new team of security researchers tasked with helping Microsoft ship the most secure products in the new domain of Adaptive cloud. The ideal candidate will have broad experience in both cloud security research as well as OS and container security, and with leadership qualities to lead additional researchers in hands on research.
Responsibilities

Build a new security research team from the ground up, defining the targets and roadmap.
Lead the work of a security focused research team across multiple concurrent projects.
Perform strategic and independent security research, discover new bug classes and offensive techniques.
Collaborating with partner teams and organizations on research findings to maximize security impact.
Contribute to improving the security posture of Adaptive cloud products.
Evaluate tools, techniques, and procedures for feasibility, effectiveness, and impact.
Develop and refine offensive security knowledgebases, guides, and training.
Contribute to tooling and automation for security research, including bug discovery automation and analysis.

As a Cloud Solution Architect aligned to the Azure AI platform for Microsoft's Customer Experience & Success (CE&S) organization, you will enable customers to achieve their outcomes based on their investments in Microsoft technology. Leveraging your Microsoft Azure Artificial Intelligence (AI) and Machine Learning (ML) technical subject matter expertise, you will lead technical conversations with customers and Microsoft colleagues, driving value to their organization. This is a hands-on role that includes accelerating customer adoption by building Generative AI solutions and identifying resolutions to unblock customer success projects. You will also drive product influence with Engineering through technical feedback and increase technical intensity with the Field teams. This opportunity will allow you to accelerate your career growth, honing your technical and collaboration skills, and deepening your cloud expertise.

The Microsoft Customer Experience & Success (CE&S) organization is responsible for the strategy, design, and implementation of Microsofts end-to-end customer experience. Come join CE&S and help us build a future where customers come to us not only because we provide industry-leading products and services, but also because we provide differentiated and connected customer experience.

Microsofts mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond
Accelerate new customer opportunities from Proof of Concept (PoC) to Production for Azure AI workloads such as Azure OpenAI, Azure Cognitive Search, and Azure Machine Learning
Provide customers with Generative AI expertise and advisory through architecture designs, proof of concepts, production reviews, and post-sales support.
Expand and grow Azure AI adoption for existing customer base.
Lead and provide guidance in technical escalations to unblock AI customers in collaboration with Microsoft stakeholders.
Provide expert perspective to key stakeholders to increase platform adoption through customer insights & feedback on a regular basis.
Drive product improvement and influence roadmap with Engineering through deep technical insights.
Increase AI technical intensity in the field through upskilling, sharing best practices, and scaling repeatable IP

The Financial Integrity (FI) Risk team is part of the Meta FinTech (MFT) engineering teams dealing with Risk, Compliance and Care (AKA Financial Integrity). The team is responsible for detecting and stopping financial risk (e.g. fraud) throughout the customer and business journey.

Our Tech:
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.
Machine Learning Engineer - Financial Integrity Risk 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

Meta is building the Metaverse, the "spatial internet" where immersive virtual worlds will coexist with the real world. Augmented and Mixed reality will transform the way people come together to interact, work and play. By developing new hardware and software products capable of understanding the real world and the user within their environment, we aim to make it possible for people to interact with content in their environment and share it with others.

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

The Reality Labs organization at Meta is helping more people around the world come together and connect through world-class Augmented and Virtual reality (AR/VR) products. With global departments dedicated to research and development, AR/VR is committed to driving the state of the art forward through relentless innovation. The potential to change the world is immense - and were just getting started.

Our team is researching and developing state of the art Computer Vision, 3D reconstruction and Generative AI technologies for solving challenges that bridge virtual and real worlds.

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.
Computer Vision Engineer Responsibilities
Lead the design and development of novel computer vision and/or machine learning algorithms in areas such as: Generative AI, 3D reconstruction, 3D asset manipulations, as well as, segmentation, face tracking, body tracking, key point estimation, depth sensing, 3D stereo and volumetric reconstruction, and virtual try-ons.
Play a critical role in the definition and execution of long-term roadmaps in partnership and cross functional organizations in machine learning and computer vision.
Lead and collaborate with multidisciplinary engineering and research teams to develop technologies from early exploration and incubation to production.
Develop custom/novel architectures, define use cases, and develop methodology & benchmarks to evaluate different approaches
Participate in cutting edge research in computer vision that can be applied to AR/VR product development.
Be a go-to person to escalate the most complex online / production performance and evaluation issues that require an in depth knowledge.
Mentor other AI Engineers & improve the quality of AI work in the broader team

The FI Advertiser Risk team is part of the Meta FinTech (MFT) engineering teams dealing with Risk, Compliance and Care (AKA Financial Integrity). The team is a part of the Risk pillar which leads risk mitigation throughout the customer and business journey.

The team's focus is the Ads Fraud Risk domain, and its goal is to mitigate fraud and reduce friction for advertisers. We work on boosting our fraud detection with additional policies, algorithms and ML solutions and improving detection of scaled fraud attacks.
We are building our next generation Fraud detection system as part of FI's Risk-as-a-Service effort.

Our Tech:
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

Meta is seeking AI Research Scientists to join fundamental and applied research teams, which are focused on making significant progress in AI. Advances in AI are key to our mission, spanning some of the most pressing research challenges of our generation across such areas as artificial intelligence, machine learning, computational statistics, and applied mathematics, particularly including areas such as deep learning, generative models, reinforcement learning, computer perception, natural language processing and speech. The ideal candidate will have a keen interest in producing new science to understand intelligence and technology to make computers more intelligent, or to work on more applied problems across the company.
AI Research Scientist Responsibilities
Lead and collaborate on research projects within a globally based team to advance the science and technology of intelligent machines
Lead research that enables learning the semantics of data (images, video, text, audio, speech and other modalities)
Work towards long-term ambitious research goals, while identifying intermediate milestones
Drive team goals & technical direction to pursue opportunities that make your larger organization more efficient
Influence progress of relevant research communities by producing publications
Contribute research that can be applied to Meta product development
Devise better data-driven models of human behavior

The Reality Labs organization at Meta is helping more people around the world come together and connect through world-class Augmented and Virtual reality (AR/VR) products. With global departments dedicated to research and development, AR/VR is committed to driving the state of the art forward through relentless innovation. The potential to change the world is immense - and were just getting started.
Our team is researching and developing state of the art Computer Vision and Generative AI technologies for solving challenges that bridge virtual and real worlds.

We are seeking a Research Scientist to join our team.
Individuals in this role are expected to be recognized experts in Artificial Intelligence and Machine Learning, particularly in areas such as Deep Learning, Computer Vision, and GenAI.
As a Research Scientist, you will help us develop innovative models and algorithms and apply them to large-scale production Computer Vision tasks.
Research Scientist, Computer Vision Responsibilities
Help build the next generation of generative AI/neural rendering models.
Harness passion for technology and propose new research roadmaps which can lead to the creation of new models that can be applied to multiple applications.
Make use of advanced knowledge in designing novel deep networks.
Without direct assistance, design lightweight deep networks that can be used on-device
Employ deep knowledge in the following areas: data processing, neural rendering, generative AI, sparse networks.
Disseminate findings of research by publishing papers in high impact conferences