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The Senior Software Engineer will design, develop, test, and release software for AI/ML infrastructure, collaborate with other team members, review code, document processes, and ensure systems are efficiently deployed on AWS and Kubernetes. They will drive professional development and stay updated on the latest technologies.

As a Senior Software Engineer on the ML Platform Engineering team, you’ll develop and expand core infrastructure for AI/ML products. Responsibilities include technical contributions in design, documentation, collaboration within a cross-functional team, and ownership of deployed services in AWS and Kubernetes.

The Senior Manager of Machine Learning Engineering oversees the productionization and deployment of ML models to optimize manufacturing costs and pricing. This role involves leading a team of engineers, ensuring high standards in model performance and scalability, collaborating with cross-functional teams, and driving continuous improvement in engineering practices.

As a Principal ML Engineer at Xometry, you will lead the machine learning platform engineering team, focusing on the development and implementation of infrastructure for AI/ML solutions. Responsibilities include mentoring engineers, conducting code reviews, ensuring operational excellence, and promoting innovation in machine learning technologies.

The Senior Software Engineer will be responsible for designing, coding, testing, and releasing software for Xometry's instant-quote product. This role requires collaboration with engineers and product managers, operational accountability for services, and a commitment to personal growth in a fast-paced environment.

Senior Software Engineer role at Xometry, responsible for designing, coding, testing, and releasing functionality to customers on a 1-year contract. Requires 5+ years of experience in software development, knowledge of HTML, CSS, DOM web programming, and proficiency in React, Python, and Rust.

The Senior Data Scientist will develop and fine-tune Visual Language Models (VLMs) for multimodal document understanding, implement data preparation processes, leverage transfer learning, and collaborate with teams to enhance model performance and deployment.