Top Remote Machine Learning Jobs in Providence, RI

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

The Machine Learning Solutions Architect designs and implements data solutions, overseeing model deployment and ensuring robust performance and integration within customer environments.

Design and implement ML systems for ads targeting, drive technical direction, mentor junior engineers, and collaborate cross-functionally for product development.

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

As a Senior Machine Learning Engineer, you will develop and deploy AI-driven products to enhance clinical trials, collaborating with teams to improve patient care through scalable machine learning solutions.

The Staff Product Manager will define and lead ML-driven ranking, targeting, and personalization systems for Fetch's ads business, working with cross-functional teams to optimize user experiences and advertiser outcomes.

Join a team focusing on end-to-end ML Ops for the insurance sector. Develop and deploy ML solutions, build infrastructure, and solve business challenges using advanced technologies.

The role involves sourcing and recruiting senior engineers in AI, ML, and related fields, building talent pipelines, and collaborating with engineering leadership.

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.

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

Lead the development of AI systems for vulnerability detection and penetration testing with expertise in AI/ML technologies and cybersecurity principles.

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.

Develop AI tools using statistical modeling and establish pipelines for causal inference and predictive analytics to assist political campaigns.

Develop and optimize machine learning models for processing financial documents, mentor junior staff, and collaborate across teams to enhance software products.

The Senior Staff AI/ML Engineer will design, develop, and deploy scalable machine learning models, collaborating with teams to address business challenges and ensure high performance in production environments.

You will take ownership of ML Ops systems, improve CI/CD processes, and enhance software infrastructure for ML Engineers providing real-time intelligence products.

As a Senior Software Engineer in Machine Learning, you'll develop and maintain machine learning models for geospatial analytics, collaborate with teams to refine algorithms, and ensure high-quality deployment of models at scale.

A Machine Learning Engineer will design and optimize AI components, develop datasets, evaluate models, and collaborate cross-functionally to enhance AI insights.

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.

Develop and tune ML models, productionize and deploy strategies for ML systems, and align cross-functionally to communicate performance metrics.

Lead design and development of AI/ML demos on Snowflake platform, collaborating with product teams to align with releases and providing technical expertise in data science and MLOps.

The Senior ML Engineer will design evaluations for AI features, optimize pipelines, and ensure quality in ML systems while collaborating with cross-functional teams.

The role involves managing data pipelines for 3D generative models, developing infrastructure, collaborating with research teams, and ensuring data quality.

Collaborate to scale self-driving systems, build training frameworks, profile model performance, and evaluate emerging technologies for training and inference.

Lead the design and deployment of scalable ML infrastructure, focusing on MLOps and LLM strategies, while mentoring team members and ensuring operational maturity.