Top eCommerce Machine Learning Engineer Jobs in Boston, MA

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.

The Senior Machine Learning Engineer will develop and optimize ML infrastructure, create data pipelines, and model orchestration ensuring data quality and performance monitoring.

Design, develop, and deploy machine learning models and AI features. Build and optimize ML pipelines for various use cases.

The Senior Machine Learning Engineer at Grailed will enhance personalization and recommendations, develop AI/ML solutions, and collaborate across departments for data-driven insights.

Build and own measurement systems, MMM, incrementality, and experimentation frameworks to quantify marketing incrementality, optimize budget allocation, forecast LTV/CAC, and operationalize production-grade models and metrics to drive growth, retention, and long-term enterprise value.

The Machine Learning Engineer will design and build systems that improve the AI shopping agent by using real user feedback, focusing on feedback loops and metrics to enhance user interactions and agent performance.

Design and scale machine learning systems and infrastructure, partner with data and applied scientists, build reusable ML pipelines and abstractions, ensure production readiness using robust software engineering practices, and prototype new technologies to improve customer experience and automate business processes.

Lead Shipium's Data Science, AI, Data Engineering, and BI teams to define multi-year ML/data strategy, run production ML and data platforms, scale hiring and org design, ensure model lifecycle and observability, and own Generative AI infrastructure, evaluation, and vendor decisions to deliver production-grade supply-chain AI capabilities.

Lead a team of machine learning data scientists, overseeing model development for fraud detection and risk assessment, and ensuring quality throughout the model lifecycle while mentoring the team.

Design, iterate, and productionize ML models for recommendation, ranking, routing, and monetization. Lead A/B testing and causal measurement, build dashboards and analytics, define metrics, collaborate with Product and Engineering, and scale ML/analytics infrastructure and model lifecycle management.