Top Remote Machine Learning Jobs in Hartford, CT

The role involves developing advanced machine learning solutions, collaborating with experts, and managing ML model architecture and deployment.

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.

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

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.

The Machine Learning Engineer will design, train, and evaluate computer vision models, collaborate on engineering improvements, and deploy ML systems for surgical workflows.

The Machine Learning Engineer will build and scale ML infrastructure, drive model performance, and ensure reliability while collaborating with various teams to optimize feature engineering pipelines and champion best practices.

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

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 Machine Learning Engineer will develop AI products by designing and deploying ML models, optimizing pipelines, and collaborating with teams.

Design and deploy scalable enterprise software solutions, manage cloud resources, optimize data-driven platforms, and integrate advanced ML models and workflows.

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

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

Design and deliver customer experience solutions using machine learning techniques, analyze customer data, implement ML algorithms, and automate processes.

As a Senior Data Scientist, you will develop models to extract insights from complex datasets, focusing on temporal and geospatial features, while collaborating with engineering teams to implement solutions.

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.

Lead ML Performance Optimization for autonomous driving, collaborating across teams, implementing optimization techniques, and mentoring engineers for career growth.

The ML Engineer will leverage deep learning for natural language processing tasks, impacting Entefy's products while requiring strong machine learning fundamentals and NLP expertise.

As a Senior Machine Learning Engineer, you will build and productionize ML models, develop scalable infrastructure, and mentor teams to optimize marketing products.

As a Senior Machine Learning Engineer, you will design and deploy object detection models, collaborate with teams, and enhance ML solutions.

The role involves designing and implementing cloud-native infrastructure for AI/ML workloads, optimizing Kubernetes environments, and contributing to open-source MLOps tooling.

Lead a team of data scientists developing ML-driven products, managing infrastructure, training models, and ensuring production quality while collaborating with senior leadership.

Design and implement machine learning perception systems for autonomous ground vehicles. Work on deep learning models, optimize for edge devices, and collaborate across teams.

Lead a team of ML Engineers to develop technology for healthcare, emphasizing hiring talent, technical leadership, and collaboration with stakeholders.

Work with the product team on automating data ETL pipelines, deploying ML models, and connecting ML models to user interfaces. Collaborate with cross-functional teams on product development.