Top Remote Machine Learning Jobs in Hartford, CT

Lead efforts in building and maintaining ML platforms, improve metrics pipelines, and manage data collection for perception systems. Collaborate with cross-functional teams and ensure workflow reproducibility and traceability.

The Senior ML/AI Engineer designs and implements ML/AI solutions, leading end-to-end ML/AI pipelines, collaborating with teams, mentoring, and developing infrastructure for large-scale data-driven products.

Lead research and development of advanced fraud detection ML models, experiment design, productionizing prototypes, and mentoring engineers.

The role involves improving recommendation systems using machine learning, developing end-to-end ML systems, and collaborating with engineers.

The Senior Product Manager will develop AI tools, collaborating with research, engineering, and design to meet user needs and drive business value.

The Senior Machine Learning Engineer at SmarterDx will deploy, maintain, and optimize machine learning models, collaborating across disciplines to integrate these solutions and improve clinical AI outcomes.

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

The role involves building deep learning models for ad candidate retrieval and ranking, improving recommendation systems, and taking ownership of ML systems.

As a Machine Learning Customer Success Engineer, you will facilitate ML workflows, manage user relationships, and contribute to FiftyOne's development.

As an AI Specialist SC, you will support major global clients by offering presales machine learning solutions, driving business transformations and technical discussions, while collaborating across teams.

The Senior Principal AI/ML Architect will drive AI architectural vision, influence multi-squad strategy, design intelligent systems, and set operational standards for Yahoo Mail's AI capabilities.

As a Machine Learning Engineering Intern at Airbnb, you will drive an AI-focused project, collaborate across teams, and contribute to search relevance and personalization initiatives.

The Machine Learning Engineer will design, build, and maintain scalable ML systems, optimize ML pipelines, implement MLOps, and collaborate with teams to deploy models into real-time products.

The AI / ML Solutions Engineer will implement and scale machine learning workloads using Ray, advise customers on ML architecture, and enable MLOps teams through technical guidance and support.

The Senior Machine Learning Engineer will manage the lifecycle of ML models, transform prototypes to production code, build training pipelines, and monitor system performance.

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

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

As a Senior AI Engineer, you will develop and optimize Generative AI applications, manage AI pipelines, collaborate with clients, and ensure production delivery with MLOps best practices.

As a Senior Machine Learning Engineer, you will drive AI initiatives, collaborate across teams, and develop scalable ML infrastructure to improve healthcare.

The Lead ML Ops Data Scientist will design scalable MLOps systems, mentor data scientists, and develop models for rental matching and personalization.

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

As a Machine Learning Engineer, you'll design and implement detection systems, enhance model efficacy, and analyze datasets to improve email detection against security threats.

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.

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