Top Hybrid Machine Learning Engineer Jobs in Boston, MA

As a Lead Machine Learning Engineer, you'll design and implement machine learning applications, collaborate with teams, ensure model performance, and build cloud-based infrastructures.

The Staff Machine Learning Engineer will design and build scalable ML systems for health insights, collaborating with cross-functional teams to ensure compliance and quality in production-ready services.

The Senior Machine Learning Engineer will design and implement ML systems, oversee data pipelines and model management, and guide technical strategy while collaborating across teams to deliver production-ready models.

The Machine Learning Engineer will design, implement, and manage complex ML algorithms, focusing on computer vision and data analysis while collaborating with team members to transition algorithms to production.

The role involves developing AI/ML models for bioprocesses, optimizing cell culture for recombinant protein production, and conducting experiments for model validation.

The Principal Data Scientist will design and develop machine learning systems, manage data pipelines, and collaborate with teams to improve products and services. This role requires significant experience in ML model deployment and communication with stakeholders.

As a Lead AI Engineer, you will design, develop, and scale backend and ML systems, mentor engineers, and collaborate to create predictive ML solutions.

The Applied ML Engineer will design, train, and evaluate machine learning models, optimize inference pipelines, and collaborate with cross-functional teams to deploy AI solutions.

Design, train, and deploy machine learning models for RF signal detection, classification, and vessel activity tracking in maritime intelligence.

The Senior Engineer for Autonomy ML Systems will design and evaluate metrics for machine learning model performance and system behavior to improve safety and performance in driverless technology.

The Machine Learning Scientist will build and integrate ML models, analyze data, design scalable pipelines, and ensure robust model testing.