דרושים » דאטה » משרת ML engineer /מוביל ML Engineering

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 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

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.