Top Senior Machine Learning Jobs in Richmond, VA

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

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

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

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.

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.

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.

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

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.

Lead a team of ML engineers and software engineers focused on Ads Content Understanding, driving innovation and efficiency in Ads Monetization.

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

The Staff Software Engineer will lead the design and architecture of the AI/ML platform, mentor engineers, and drive technical direction for MLOps infrastructure in healthcare automation.

Lead the architecture and development of AI and ML features, design evaluation frameworks, and mentor team members to enhance ML-driven product experiences.

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