Top Machine Learning Jobs in Hartford, CT

The Lead Data/ML Engineer will design and maintain scalable data pipelines and AI systems, ensuring data reliability and collaboration with product teams.

Lead ML research and development, collaborating with engineers and clinicians to create enterprise-grade products and establish a research agenda while sharing insights through publications and presentations.

The Staff Machine Learning Engineer will design, develop, and deploy scalable ML algorithms, lead experimentation, and collaborate across teams to enhance AI solutions.

The Senior Product Manager will lead the AI-driven product roadmap at Chess.com, focusing on innovative chess tools and deepening user engagement through AI features.

Lead the design and delivery of ML infrastructure and models, mentor engineers, and drive innovation in AI-powered intelligence at Upwork.

As a Machine Learning Engineer at Yobi, you will design and optimize systems for real-time model serving and inference, ensuring performance and reliability.

The Senior Machine Learning Engineer will develop full-stack tools, collaborate on research, and build production ML systems, optimizing infrastructure for AI startups.

Develop and deploy machine learning models for fraud detection and prevention, collaborating with teams and ensuring model performance over time.

The Sr. Engineering Manager will oversee Reddit's Ads Marketplace Quality, focusing on optimizing auctions and delivery systems while managing team growth and development.

The Staff Software Engineer will lead the design and architecture of the AI/ML platform, mentor engineers, and drive technical direction for MLOps infrastructure in healthcare automation.

As a Principal Machine Learning Engineer, you will lead a team in developing machine learning solutions for ultrasound applications, mentor engineers, and ensure high-quality regulatory-compliant product delivery.

The Senior ML Backend Engineer will design and maintain backend services, integrate ML models, and ensure compliance and performance within healthcare workflows, while leading technical initiatives and mentoring team members.

As a Machine Learning Engineer, you'll develop and deploy ML models for medical imaging, collaborate with teams, and optimize for production environments.

Seeking a build engineer with strong software engineering skills to manage CI/CD systems and establish best practices for large-scale monorepos for ML applications.

Build a next-generation ML data and feature infrastructure platform to enhance the productivity of ML practitioners and improve feature accessibility.

Lead the design and deployment of machine learning models for personalized healthcare, mentor engineers, and collaborate across teams to improve ML infrastructure.

Lead the architecture and development of machine learning systems for personalized care, treatment intelligence, and automation at scale. Collaborate with cross-functional teams and mentor engineers.

Join Netflix's Algorithms team as a Machine Learning Software Engineer to design and develop solutions for personalization systems. Collaborate on scalable machine learning algorithms and enhance software engineering practices.

As an ML Engineer, you will build and productionize predictive models for ad inventory and campaign performance, contributing to the growth of Netflix's ad platform.

The Principal Machine Learning Engineer will architect and scale ML systems for personalized healthcare. Responsibilities include system design, optimization, technical leadership, and mentoring within the team.

Develop and deploy machine learning models for real-time ad systems, optimizing inventory management, targeting, and campaign effectiveness for Netflix's advertising platform.

As a Sr. Machine Learning Engineer, you will develop and deploy ML models, enhance care personalization, and mentor teams while optimizing workflows.

Responsible for building scalable microservices and data pipelines, mentoring junior engineers, and ensuring data quality for ML training.

Lead a team of ML engineers and software engineers focused on Ads Content Understanding, driving innovation and efficiency in Ads Monetization.

Design and implement ML infrastructure for scalable deployment, collaborate across teams, optimize ML systems, and enhance tooling.