Top Machine Learning Jobs in Dallas, TX

Lead the CVS Health Automation COE, driving strategy for automation technologies, managing teams, vendors, budgets, and fostering innovation in automation processes.

The SVP will develop and implement a comprehensive fraud program, leading a team to mitigate fraud risks and optimize operational efficiency while ensuring exceptional customer experiences.

As a Design Technology Data Scientist, you will lead innovative solutions in data analysis and machine learning, collaborating with teams to improve design processes and insights.

In this role, you'll design and implement machine learning models, collaborate with teams, and maintain ML applications, focusing on AWS infrastructure and best practices.

The role manages teams developing strategies to understand risks, optimize performance, and drive analytics for fraud prevention, while building relationships across departments.

Lead the design and implementation of AI-driven workflows and automation solutions to improve operational efficiency and customer experience in healthcare.

Lead the Mobility team in creating machine learning solutions, manage engineering staff, and drive product strategy and development.

The role leads the development of anti-money laundering detection functions and operational risk practices, overseeing models and project management in financial crime risk management.

The Senior Architect will lead software engineering projects, mentor teams, and help clients modernize their technology stack using innovative software solutions and AI-driven capabilities.

The Associate Director will manage the eDiscovery consulting team, oversee projects, and improve processes while advising on cutting-edge eDiscovery technologies.

As a Senior ML Ops Engineer, you will implement scalable systems, deploy ML models, manage infrastructure, and collaborate with teams for MLOps best practices.

The role involves building and maintaining MLOps pipelines, collaborating with teams to deploy ML models, and ensuring high performance of ML services.

The role involves collaborating with teams to design and deploy ML models, develop scalable AI applications, and optimize performance using GCP and Vertex AI.

Assist in machine learning projects by applying algorithms, preprocessing data, and evaluating models. Collaborate in a team to develop new techniques.

Lead development and integration of AI/ML models and testing frameworks, overseeing quality assurance and enhancing existing automation processes.

Design and optimize machine learning pipelines for large-scale systems, integrating models into user features and ensuring data integrity and performance.

Lead the design and optimization of ML systems at Scribd, managing cross-team projects, mentoring engineers, and shaping technical direction for ML initiatives.

Design and optimize machine learning systems, build scalable data pipelines, and run experiments to improve recommendations for Scribd's products.

Pioneering machine learning solutions for the defense sector, developing and deploying production-grade ML models, tackling complex data challenges, and collaborating with engineers.

The role involves designing and developing machine learning models, collaborating with teams to implement scalable solutions, and maintaining production-quality code.

As a Lead Machine Learning Engineer, design and maintain scalable ML solutions, optimize workflows, evaluate model performance, and collaborate with cross-functional teams to integrate complex data-driven applications, ensuring high reliability and performance standards.

The role involves overseeing machine learning model lifecycle, optimizing performance, managing CI/CD pipelines, mentoring junior engineers, and ensuring security best practices.

The ETL Developer will design and optimize data integration pipelines, leverage AI/ML technologies, and collaborate with teams to ensure data accuracy and efficiency.

Design and implement machine learning solutions, write clean code, and collaborate with teams to build scalable ML systems for real-world applications.

The role involves designing, building, and optimizing large-scale ML systems for personalized recommendations, focusing on data ingestion, model training, and collaboration with cross-functional teams.