Top Machine Learning Jobs in Raleigh, NC

As a ML Ops Engineer, you will optimize model performance, monitor inference costs, and implement scalable solutions for AI applications.

The Lead Data/ML Engineer will design and maintain scalable data pipelines and AI systems, ensuring data reliability and collaboration with product teams.

The Senior Product Manager will lead the strategy for an AI assistant, ensuring compliance and user satisfaction while coordinating with cross-functional teams throughout product development.

The Machine Learning Engineer will work on sensing and perception algorithms, deploying computer vision algorithms, and collaborating for code testing and evaluation.

Lead research on humanistic AI for autonomous systems. Collaborate with teams, design experiments, and publish findings in top-tier publications.

The Applied Scientist Intern will develop and deploy AI models, build ETL pipelines, and collaborate to enhance AI quality in legal tech.

This role involves designing, developing, and scaling machine learning systems for payment flows and recommendations, leading technical initiatives, and optimizing complex financial decision systems.

The Staff Machine Learning Engineer will design, develop, and deploy scalable ML algorithms, lead experimentation, and collaborate across teams to enhance AI solutions.

Lead the design and implementation of machine learning platforms at Yahoo, focusing on architecture, governance, and system performance while mentoring teams and influencing industry trends.

Develop and implement machine learning models, analyze data, collaborate on solutions, and optimize systems for scalability while staying updated with AI advancements.

The Staff Machine Learning Engineer will develop and maintain MLOps tools, optimize model performance, and collaborate on ML system architecture with a focus on enhancing developer experience.

Lead ML research and development, collaborating with engineers and clinicians to create enterprise-grade products and establish a research agenda while sharing insights through publications and presentations.

The Staff Software Engineer will design, build, and scale ML systems to enhance user engagement through recommender systems and collaboration with cross-functional teams.

The Senior Machine Learning Engineer will develop ML infrastructure, deploy ML models, monitor performance, and mentor junior engineers. Collaboration with cross-functional teams is essential.

The Senior Machine Learning Engineer will design advanced AI/ML solutions, build product features, and collaborate in an Agile environment using cutting-edge technologies.

Lead the design and delivery of ML infrastructure and models, mentor engineers, and drive innovation in AI-powered intelligence at Upwork.

As an Associate Technical Architect in Machine Learning, you will design and develop advanced ML models on AWS, ensuring scalability and reliability while collaborating with cross-functional teams.

As a Machine Learning Engineer at Netflix, you will forecast traffic, optimize infrastructure, and manage ML models, supporting engineering teams for improved performance and reliability.

The Forward Deployed ML Engineer will integrate ML models into clinical software, support customers with implementation, and collaborate across teams for optimal solutions in medical imaging.

As a Senior ML Platform Engineer, you will develop AI-driven solutions, enhance model performance, and collaborate across teams to optimize data processes.

Drive cross-team engineering efforts to improve user experience on Homefeed by implementing large-scale machine learning systems and enhancing data processing efficiency.

As a Research Engineer for Applied ML/AI, you'll bridge research and production, manage training and fine-tuning pipelines, optimize multimodal features, and ensure high-performance model integration in healthcare.

Design and optimize ML models for AI-powered facial analysis, focusing on computer vision and generative models, ensuring accuracy and efficiency in a B2B SaaS environment.

Design and implement ML infrastructure for scalable deployment, collaborate across teams, optimize ML systems, and enhance tooling.

Responsible for building scalable microservices and data pipelines, mentoring junior engineers, and ensuring data quality for ML training.