Top Hybrid Machine Learning Engineer Jobs

The Senior AI/ML Engineer will design and implement scalable ML infrastructure solutions, mentor junior engineers, and own key technical projects.

As a Senior Machine Learning Engineer, you will develop machine learning models, manage their lifecycle, build infrastructure, and collaborate with teams to enhance our capabilities in various ML areas.

The role involves developing and implementing machine learning models to combat account takeover fraud, collaborating with a cross-functional team, and leveraging large data sets.

The Senior Lead AI Engineer will develop, optimize, and support AI products, collaborating with teams to implement machine learning solutions transforming customer interactions at Capital One.

As a Distinguished Machine Learning Engineer, you will lead ML initiatives, mentor engineers, and produce state-of-the-art ML applications for fraud prevention in the financial sector.

The Staff Machine Learning Engineer will develop ML systems for fraud prevention, lead signal discovery, and collaborate across teams on lending decision frameworks, mentoring peers, and owning solutions from design to deployment.

As a Senior Machine Learning Engineer in the Health team, you'll design and productionize ML systems for personalized health metrics, collaborating with data scientists and engineers to optimize performance and ensure service reliability.

Responsible for developing AI/ML solutions, managing teams, understanding product requirements, and creating data processing pipelines to enhance enterprise services.

Lead the design and development of ML solutions for fraud detection across travel and expense, mentor junior team members, and collaborate on fraud strategy.

Support development and implementation of machine learning algorithms, analyze large and sensor datasets, perform EDA and feature engineering, build ML proof-of-concept applications, and help translate models into production-ready code.

Work with R&D teams to develop, train, and evaluate ML and deep learning models for wireless communications, build data processing and feature pipelines, perform EDA and statistical analysis, integrate ML into hardware/software prototypes, automate data capture and visualization, and produce technical documentation and presentations.

Lead the development of advanced analytics and AI/ML models, involving statistical modeling and machine learning, to provide actionable insights for stakeholders and clients.