Top Remote Senior Level Machine Learning Engineer Jobs in Boston, MA

Lead and implement AI/ML platform solutions to enhance patient care by developing scalable systems, mentoring engineers, collaborating with cross-functional teams, and leveraging emerging technologies.

The Senior Machine Learning Platform Engineer will build foundational components for ML models, mentor junior engineers, and optimize streaming pipelines. Responsibilities include enhancing ML infrastructure and ensuring data quality.

The Senior Applied Scientist II is responsible for designing and implementing ML models to optimize the supply chain, collaborating across teams, and driving data-driven decision-making.

The Senior Machine Learning Engineer will develop ML APIs, build data pipelines, and collaborate across teams to enhance ML models and applications.

The Senior Machine Learning Engineer will design, implement, and optimize machine learning models, drive AI initiatives, and collaborate cross-functionally to enhance SailPoint's AI capabilities.

Lead the SDL and Cash Line ML teams, developing innovative machine learning models to improve lending solutions and expand credit access. Collaborate with cross-functional teams to drive product strategy and enhance model performance, while mentoring a team of ML scientists.

The Engineering Manager, Machine Learning will lead a team focusing on 3D deep learning and computer vision, manage the ML roadmap, and implement best practices for MLOps pipelines.

The Principal ML Engineer at Agero will develop and productionize a Dispatch System using ML models, optimize decision-making processes, and lead a team to improve operational performance.

Lead AI/machine learning innovations and advanced analytics for new vehicles. Develop statistical models for pricing and valuation, guiding experiments and communicating insights.

Lead the development of fraud prediction models using machine learning, collaborating across teams to build, scale, and monitor models in production.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.