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

Lead end-to-end data science projects: build, deploy, and monitor ML models (credit risk, pricing, collections, fraud). Partner cross-functionally, productionize pipelines, write production-grade code, and communicate results to technical and non-technical stakeholders.

Lead design, build, and scale of production ML systems (including supervised, unsupervised, and LLM-based models). Translate prototypes into cloud-native deployments, optimize model performance, implement MLOps practices, and collaborate cross-functionally to integrate AI features while promoting model governance, explainability, and responsible AI.

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

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.

Build and optimize machine learning models for 3D dental applications, research state-of-the-art methods, and implement MLOps best practices.

Lead design and development of ML systems for restaurant technology, including menu recommendations and demand forecasting, influencing strategic decisions and enhancing customer experiences.

Lead the design, development, and deployment of machine learning models, collaborating with teams and mentoring engineers. Focus on scalable ML workflows.

Lead development of machine learning models for real-time content ranking, translating business objectives into effective ML solutions while managing experiments and data pipelines.

Design and build ML features using LLMs, collaborate on technical architecture, and ensure safety and performance of deployments.

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.