Top Hybrid Machine Learning Engineer Jobs

Lead the technical roadmap for the Ad Marketplace, design and optimize machine learning models, and collaborate with teams to meet objectives.

Lead a team to build large-scale machine learning indexing and ranking systems while collaborating with product teams and enhancing team performance.

Lead research engineering projects in user modeling and personalization, prototype and ship ML systems (recommendation, retrieval, generative models), collaborate with researchers and engineers, publish findings, and scale solutions to millions of users.

Design and build ML models, optimize data pipelines, lead teams, and support AI software components, while advancing AI capabilities for improved customer experiences.

Lead the design and implementation of machine learning applications, collaborate with cross-functional teams, optimize ML systems, and ensure responsible AI practices.

Designs, implements, and optimizes machine learning and deep learning algorithms for product features and system improvements. Translates statistical models into production code, works with large-scale computing frameworks and data analysis systems, and performs testing, debugging, documentation, and maintenance for ML-driven solutions.

The Senior ML/AI Software Engineer will build analysis algorithms and maintain evaluation dashboards for GM's autonomy stack performance, leveraging large datasets and statistical methods.

As a Senior Machine Learning Engineer, you will develop machine learning models, automate data pipelines, and collaborate with teams to meet customer needs.

Design and deploy scalable machine learning models, analyze datasets, collaborate on ML standards, and communicate solutions to stakeholders.

Design and maintain ML pipelines and data pipelines, optimize fraud detection models, collaborate with teams, and ensure data quality for high-scale applications.

The role involves designing and deploying machine learning systems for a search platform, focusing on ranking and retrieval while collaborating with cross-functional teams.

Lead the development of machine learning systems for fraud detection and risk modeling at scale, collaborating across teams and influencing ML strategy.

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