Software Engineer, Machine Learning at Meta focused on developing and testing operating systems-level software, compilers, and network distribution software for massive social data and prediction problems. The role involves applying deep learning, data regression, and rules-based models to classification and optimization tasks such as fraud detection, ad ranking, recommendation systems, and spam detection, with a focus on building highly scalable and efficient systems.

Machine Learning Engineer at Meta focused on designing, implementing, and productionizing ML models for ad ranking, targeting, and conversion optimization. This role involves leading experiments, building scalable ML pipelines, and collaborating with cross-functional teams. Requires a Master's degree and 3 years of experience in ML, deep learning, distributed computing, and Python/C++.

Software Engineer, Machine Learning at Meta focused on developing and testing operating systems-level software, compilers, and network distribution software for massive social data and prediction problems. The role involves working on classification and optimization problems using deep learning, data regression, and rule-based models to build highly scalable systems and algorithms.

Meta is seeking a Sr. Data Scientist to work with large datasets, solve complex problems using analytical and statistical approaches, and partner with Product and Engineering teams. The role involves informing product decisions, working with data infrastructure (Hadoop, Hive, MySQL), authoring pipelines (SQL, Python ETL), guiding experimentation, and developing machine learning models including label design and evaluation. The scientist will leverage Python and R to make strategic business decisions, apply causal inference, design/evaluate experiments, monitor metrics, build dashboards, and develop models of user behavior. This includes influencing product teams through presentations, communicating results, and spreading best practices. The role requires applying predictive modeling, machine learning, and experimentation/causal inference methods regularly.

Machine Learning Engineer at Meta focused on developing and deploying scalable ML models for consumer-facing problems like fraud detection, ad/feed/search ranking, and spam detection. The role involves research, design, development, testing, and production deployment of these models, working with data pipelines, and collaborating with cross-functional teams.

Meta is seeking experienced Software Engineers to build and improve cutting-edge consumer products, focusing on connecting people globally. This role involves developing innovative features, optimizing performance, and architecting scalable systems, with a strong emphasis on integrating and applying AI tools responsibly to drive impact and efficiency.

This role is for a Principal Machine Learning Engineer focused on financial risk management at Meta. The individual will be responsible for strategizing and delivering ML solutions to minimize financial losses, optimize existing ML models, and lead the research and introduction of advanced technologies like deep learning and LLMs. They will also collaborate on the next-generation ML architecture, identify new business opportunities, and mentor other senior ML engineers.

Software Engineer, Machine Learning role at Meta in Singapore, focusing on building cutting-edge products that connect billions of people. The role involves developing and shipping ML models at scale, integrating AI tools to optimize workflows, and staying current with emerging AI technologies. Requires experience in ML, recommendation systems, or AI, with a strong software engineering background.

Meta is looking for a principal engineer to lead teams in building and improving AI-powered products that connect billions of people globally. The role involves driving technical direction, developing roadmaps, and mentoring stakeholders, with a focus on integrating AI tools and adhering to responsible AI practices.

Software Engineer with technical leadership responsibilities focused on applying machine learning to large-scale consumer prediction problems like fraud detection, recommendation systems, and classification. The role involves driving technical direction, developing scalable ML models, and shipping products to millions of users.