• At Xometry, you will play a crucial role in exploring new machine learning opportunities, researching and performing proof of concepts, and bringing new machine learning and AI solutions into Xometry’s platform • Develop and implement machine learning models that improve Xometry’s ability to predict cost, price, and sourcing options for our customers and suppliers. • You will be responsible for leading the evaluation of emerging technologies, identifying areas for improvement, and developing new features while ensuring the reliability and scalability of Xometry’s platform • Lead the exploration of emerging AI and machine learning technologies and develop proof-of-concepts to assess their potential impact on Xometry’s platform • Collaborate with cross-functional teams to gather requirements, prioritize features, and define technical solutions based on the latest innovations • Monitor machine learning models and AI performance and troubleshoot issues as they arise • Contribute to the documentation and knowledge base to help other teams understand and use Xometry’s AI effectively

• Building the core Masterful product. • Building state of the art machine learning models for generative data. • Developing backend infrastructure to support the cloud deployment of these models. • Conducting front-end web development to monitor and analyze the performance of these models. • Creating customer-facing APIs and documentation to train their models. • Responsible for the full lifecycle of the product, including design, development, deployment, testing, maintenance, and documentation.

• Deploy and maintain machine learning models and pipelines in production environments. • Work closely with data scientists, data engineers, and application engineers to integrate ML models into the broader SmarterDx platform. • Craft, implement, and maintain MLOps tools and practices, including continuous integration, delivery, and monitoring of machine learning systems. • Optimize model performance and scalability, ensuring high reliability and efficiency. • Build tools to improve the lives of our data scientists. • Contribute to the design and architecture of our ML systems.

• Craft, implement, and maintain MLOps tools and practices, including continuous integration, delivery, and monitoring of machine learning systems. • Optimize model performance and scalability, ensuring high reliability and efficiency. • Build tools to improve the lives of our data scientists. • Contribute to the design and architecture of our ML systems.