Top Senior Machine Learning Jobs in Raleigh, NC

Lead AI and machine learning projects, create workflows and models, analyze data from diverse sources, and present insights to non-technical audiences.

Design and maintain scalable machine learning infrastructure, collaborating with data scientists, ensuring performance, security, and compliance of AI systems.

Join DICK'S Sporting Goods as a Senior Machine Learning Engineer, focusing on AI-driven search solutions that enhance decision-making and engagement across the organization.

The Engineering Manager - Data & ML will redesign and scale Underdog's data and ML platform, establishing best practices, leading a team, and collaborating with cross-functional teams to optimize data systems.

As a Senior ML Ops Engineer, you'll build ML infrastructure, implement automated deployment, optimize model serving, and ensure production performance.

The Senior Product Manager will lead the strategy and execution of AI-driven features in a SaaS platform, ensuring high-quality user experiences and data-driven decisions while mentoring junior team members.

Lead the development of AI agents and infrastructure, collaborate with stakeholders, design APIs and SDKs, and advocate for best practices.

Design and deploy machine learning models for autonomous vehicles, focusing on data tagging, dataset curation, and model validation.

The Solutions Architect for AI/ML at Snowflake will advise clients on best practices for Data Science workloads, design solutions, and build ML pipelines. Responsibilities also include working hands-on with SQL and Python, maintaining technical expertise and collaborating with teams to improve products.

As a Senior Machine Learning Engineer, you'll design and implement ML models, develop data pipelines, and collaborate on AI features at Mural.

The role involves developing AI-driven features for observability tools, collaborating with cross-functional teams, and delivering scalable solutions.

The role involves designing, building, and managing data pipelines and machine learning solutions for veterinary care, utilizing cloud services and various programming languages while collaborating within an Agile team.

The Machine Learning Engineer will fine-tune and deploy Large Language Models, optimize ML models, maintain data pipelines, and collaborate on AI features.

The Senior Machine Learning Engineer will lead AI initiatives, develop ML infrastructure, establish data engineering best practices, and collaborate across teams to enhance healthcare technology services.

Build and deploy ML systems that detect fraud and automate security decisions, while collaborating with cross-functional teams.

Develop and optimize machine learning models for financial and operational workflows at Netflix, ensuring model reliability and integration with studio applications.

The role focuses on enhancing detection frameworks using machine learning to identify malicious activities and minimize risks for Netflix.

The ML Platform Engineer will develop and scale ML infrastructure, automate workflows, and collaborate with data scientists to deploy models efficiently in production.

The Principal Machine Learning Engineer leads the design and implementation of advanced ML systems to enhance cybersecurity capabilities, collaborating with Data Scientists and Engineers.

Develop and implement AI features across the Rocket Lawyer platform, mentor junior developers, and maintain ML models to enhance user products.

The role involves designing and implementing user-friendly interfaces, collaborating with data scientists on ML model deployment, and maintaining CI/CD pipelines for ML workflows.

The Senior ML Engineer designs and develops advanced machine learning models, collaborates with teams, monitors model performance, and implements MLOps practices for scalability.

The Senior Machine Learning Engineer will build and operate ML systems, manage the model lifecycle, and mentor team members while ensuring sustainability data accuracy.

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

The role involves designing and implementing data and AI architectures, developing data pipelines, and integrating machine learning solutions while fostering Agile methodologies and collaboration.