Top Machine Learning Jobs in Richmond, VA

Seeking a Head of Core ML Products to lead the development of AI-driven systems for improved product matching and user experience at Fetch, requiring extensive product management experience.

Lead the development and deployment of scalable machine learning models for fintech applications, collaborating with various teams to meet business objectives.

The role involves designing and scaling ML infrastructure for LLMs, including building training pipelines and deploying models in cloud and on-prem environments. Collaboration with engineers and managing GPU/TPU workloads is key.

The Machine Learning Engineer will build and scale ML models, collaborate with cross-functional teams, and improve real-time recommendations for Stitch Fix's clients.

The Senior ML Engineer will design and deploy large-scale ML systems for personalized recommendations, directly impacting user engagement and revenue. Responsibilities include building models serving 100M+ predictions daily and optimizing data processing pipelines.

The role involves building and optimizing large-scale ML systems for personalized recommendations that drive engagement, retention, and revenue, impacting millions of users daily.

As a Senior ML Engineer, you will develop and optimize ad algorithms to enhance engagement and revenue, collaborate with teams, and implement scalable ML systems.

The role involves building and optimizing machine learning systems for ad performance, collaborating with teams to enhance targeting and revenue. Responsibilities include developing algorithms, conducting experiments, and improving infrastructure for scalable ad decisioning.

The Software Engineer will develop machine learning infrastructure, optimize models, and create tools for data analysis while collaborating with a diverse team.

The role involves leading ML/AI solutions, managing technical roadmaps, collaborating with stakeholders, and driving innovation in products and services.

Design and architect AI/ML solutions, build document parsers and classifiers, maintain AI systems, and collaborate with cross-functional teams.

The Applied Scientist will integrate LLMs into production systems, develop NLP and CV models, and lead AI initiatives for wealth.com.

The ML/AI Engineer will define machine learning direction, develop automated pipelines, integrate models, and collaborate with product teams to solve user problems.

Develop, optimize, and deploy scalable ML solutions for content decisions at Netflix, utilizing advanced ML techniques and collaboration with teams.

As a Machine Learning Engineer, you'll optimize and deploy production ML models, build inference services, and collaborate closely with teams to integrate new models and enhance performance.

The Machine Learning Engineer III will design, develop, and deploy machine learning models and algorithms, mentor junior staff, and collaborate with cross-functional teams to solve complex business problems.

The Senior Machine Learning Engineer will design and maintain ML solutions for fraud detection, collaborating across teams and establishing MLOps best practices.

The Applied ML Engineer at Ideogram will turn generative models into production features, collaborate with teams to define metrics, and ensure ML systems' reliability.

Design and build recommendation and personalization systems, conduct A/B testing and causal inference, and mentor the data team in best practices.

Design and implement ML-driven solutions for user engagement and content discovery. Collaborate across teams to enhance product features using advanced machine learning models.

The role involves designing and maintaining ML infrastructure, ensuring performance and security, collaborating with ML engineers, and implementing modern AI capabilities.

Lead the design and implementation of deep learning models for biological data, focusing on model architecture, project leadership, and innovation in machine learning applications in biology.

As a Staff Machine Learning Engineer, you will develop, productionize, and deploy ML models for clinical use, ensuring real-world effectiveness through hands-on coding and infrastructure building.

Design and implement software solutions for machine learning operations, focusing on geospatial imagery analytics and MLOps software development.

Develop and support ML infrastructure for large-scale scientific workflows, improve automation processes, and contribute to product features in AI-enabled drug discovery.