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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

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 Security is looking for a Security Engineer with experience in threat modeling, TTP identification, and detection engineering. Youll work alongside Software Engineers and Offensive Security Engineers to identify critical assets, assess the top risks, and evaluate potential attacks against Meta systems. You will be working across engineering teams supporting Production and Corporate systems to develop detection and response automation leveraging both industry-standard and custom detection and response platforms. Youll generate detection ideas utilizing some of the worlds largest data sets and build on top of hyper-scale data pipelines.
Security Engineer - Surface Coverage, Detection Engineering Responsibilities
Lead cross-functional projects to improve our capabilities to effectively detect and respond to security incidents
Review security architecture of large-scale custom and commercial systems and independently propose logging, detection and prevention controls
Perform TTP-based Threat Modeling for a wide variety of assets including endpoints, mobile, servers, internal services, public & private cloud environments and networking equipment
Perform analysis against logs from a variety of sources (e.g., individual host logs, network traffic logs) to identify potential threats and detection ideas
Build response workflows and actions that auto-resolve false positives and provide context scaling our ability to investigate
Support security incident response in a cross-functional environment and drive incident resolution
Design and implement attack testing automation to validate detection coverage
Build logging pipelines using our custom datasets and infrastructure

Meta Security is looking for an Incident Response Engineer with experience in the identification, containment and mitigation of security incidents. You will be analyzing different data sources to detect, investigate and respond to internal and external threats. You will also be working with our software and production engineering teams to develop scalable systems to automate detection and remediation and help us build the next generation of security operations and response platforms.
Security Engineer, Incident Response Responsibilities
Lead security incident response in a cross-functional environment and drive incident resolution.
Contribute to Incident Response initiatives that improve Metas capabilities to effectively respond and remediate security incidents.
Perform digital forensic acquisition and analysis of a wide variety of assets including endpoints, mobile, servers and networking equipment.
Perform log analysis from a variety of sources (e.g., individual host logs, network traffic logs) to identify potential threats.
Perform security incident root cause analysis and drive implementation of containment and mitigation strategies.
Build automation for response and remediation of malicious activity.