Senior Principal, ML/AI

Xometry is a digital marketplace that connects innovators with manufacturers. They are seeking a Senior Principal in Machine Learning & Artificial Intelligence to lead significant ML/AI initiatives, drive technical roadmaps, and collaborate across departments to enhance business value and efficiency.

Responsibilities:

• Serve as the technical leader of multiple large, cross-functional ML/AI solutions with significant, lasting impact across Xometry’s business
• Define, and drive the 18-24-month ML/AI technical roadmap - balancing breakthrough innovation (e.g., generative 3D, foundation models, large-scale vision/3D pipelines) with reliable business value delivery (e.g., quoting accuracy, lead-time reduction, defect detection, cost optimization)
• Influence partner roadmaps across engineering, product, operations, and business teams: align priorities, advise on resourcing, champion ML/AI best practices
• Proactively identify and remove roadblocks for teams and projects — whether technical, operational, data-related, or resource constraints
• Mentorship of individuals and technical teams
• Act as a trusted SME with strong cross-functional partnerships: your insights and guidance will shape ML/AI infrastructure, data, model, infrastructure, and tooling decisions
• Play a leadership role in identifying areas of opportunity — e.g., using ML/AI to unlock new revenue streams (e.g., rapid quoting for new manufacturing modalities, generative design for customers), reduce cost (e.g., automated quality inspection), or optimize efficiency (e.g., 3D-geometry classification, defect detection, generating manufacturing ready models)
• Address problems adjacent to your sphere of immediate influence: proactively tackle challenges outside direct scope and champion holistic solutions
• Stay ahead of industry developments in ML, AI, generative AI, 2D/3D modeling and manufacturing tech; translate insights into the improvement of internal best practices, tooling, frameworks, model governance, data pipelines, and operationalization
• Consistently scrutinize existing work, identify opportunities for incremental growth and efficiency optimization - whether in inference latency, model retraining intervals, feature pipelines, cost of compute, or automation of manual tasks