Top Hybrid Machine Learning Engineer Jobs

Join the Risk Machine Learning organization as a Senior Machine Learning Engineer to build systems detecting fraud and enhancing safety for Cash App and Square users. Responsibilities include designing, deploying, and monitoring ML models, collaborating with cross-functional teams, and applying advanced modeling techniques to improve risk management.

As a Principal Machine Learning Engineer at Zscaler, you will lead AI/ML projects, optimize systems, and collaborate on cybersecurity solutions while mentoring engineers.

The Staff Machine Learning Engineer will lead the development of AI solutions, ensure project delivery, and manage technical teams while interfacing with customers. Responsibilities include designing data pipelines, overseeing machine learning model development, and facilitating customer engagement.

Lead development of advanced recommendation systems, driving ML innovation, maintaining ML pipelines, and collaborating across teams to enhance user engagement.

The role involves designing and implementing ML systems for pricing, managing data pipelines, and improving model performance and reliability. It requires collaboration with cross-functional teams and mentorship for engineers.

As a Machine Learning Subject Matter Expert, analyze ML systems, validate claims, evaluate model performance, and collaborate with teams to report findings.

Lead the design and deployment of machine learning models to detect fraud, collaborating with teams to optimize performance and effectiveness while integrating solutions into existing systems.

As a Machine Learning Platform Engineer, you will partner with teams to establish data science environments, oversee analytic solutions, and lead complex projects while enhancing collaboration between technology and data science teams.

Assist in developing and deploying AI and ML features, collaborate with engineering teams, and align AI initiatives with the product vision.

The Senior Machine Learning Engineer will develop foundational models for recommendations, mentor engineers, and collaborate on improving user experiences with machine learning.