Top Machine Learning Engineer Jobs in Lexington, KY

The Machine Learning Engineer will design, build, and maintain production machine learning systems, focusing on scalability, reliability, and integration with existing products. Responsibilities include developing ML pipelines, establishing ML Ops best practices, and collaborating on product capabilities.

Develop and optimize machine learning models for data extraction from tax documents, collaborate on software integration, and mentor junior team members.

Join Xometry as a Senior Machine Learning Engineer to develop and implement machine learning models, enhancing predictive capabilities and leading technological evaluations for innovation.

The role involves designing and deploying ML models to extract knowledge from enterprise data, fine-tuning LLMs/SLMs, and collaborating on a scalable AI platform.

As a Machine Learning Senior/Staff Engineer, you'll lead architectural decisions, enhance algorithms, develop features, and deploy scalable analytics and machine learning solutions, also mentoring team members.

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.

The role involves building end-to-end modeling pipelines, developing models, delivering insights to stakeholders, and collaborating with a technical team in a fast-paced environment.

Lead a team of data scientists developing ML-driven products, managing infrastructure, training models, and ensuring production quality while collaborating with senior leadership.

Lead design, development, and productionization of advanced ML models and data pipelines. Drive architecture, experimentation, validation, and best practices while mentoring ML engineers and delivering scalable, business-driving AI solutions.

Design and deliver customer experience solutions using machine learning techniques, analyze customer data, implement ML algorithms, and automate processes.

Lead development and lifecycle of yield-optimization predictive models, integrate models into the recommendation engine, improve model accuracy to drive revenue and conversion, match users to optimal offers, and manage external partner relationships while collaborating across engineering and business teams.