Top Remote Machine Learning Jobs in Hartford, CT

Design and develop scalable search ranking algorithms, collaborate with data scientists, implement ML models, and improve user matching experience.

As a Cloud Machine Learning Engineer at Hugging Face, you will build ML solutions leveraging cloud technologies, integrate models, design APIs, and contribute to documentation and community advocacy.

Develop, optimize, and deploy ML solutions to improve content strategy and performance at Netflix, collaborating with teams across analytics and engineering.

As a Senior Machine Learning Engineer, you'll build recommender systems and data pipelines, ensuring personalized experiences for users while collaborating across teams and driving engineering best practices.

Lead the design and architecture of GNN and transformer-based multi-entity embedding generation for Reddit's recommendation systems, collaborating across teams and optimizing machine learning pipelines.

Lead the Auto-Bidding team's technical vision, optimize recommendation and forecasting systems, mentor a high-performing engineering team, and drive technical projects in ad technology.

The role involves building AI search agents, creating RAG pipelines, optimizing vector search technologies, and automating evaluation processes.

The Senior Software Engineer will design and implement ML systems, collaborate with cross-functional teams, and optimize ML deployment processes.

Lead a team of Machine Learning engineers, drive technical direction, oversee large-scale ML systems and improve user experience for over 500M users.

As a Staff Machine Learning Engineer, you'll lead cross-team projects, guide teams in building scalable AI systems, and mentor engineers, leveraging expertise in diverse ML techniques.

As a Machine Learning Engineer Intern, you will design and develop AI applications, conduct machine learning experiments, and work collaboratively with engineers on client projects.

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