Senior Principal, ML/AI

12+ years of professional experience in machine learning, artificial intelligence, or data science roles — with several years in senior or principal capacity leading major programs.

Demonstrated experience architecting and delivering large scale ML/AI solutions - end-to-end from data ingestion, feature engineering, model training, evaluation, deployment, monitoring & operations.

Deep expertise in machine learning frameworks (TensorFlow, PyTorch), data engineering, model infrastructure, MLOps, cloud platforms (AWS, GCP, Azure), and scalable production systems.

Experience in 3D modeling / geometry / computer vision / generative models (e.g., point-cloud processing, mesh processing, text23D, image23D, CAD/CAM integration) is highly desirable.

Strong exposure to generative AI techniques (large language models, multimodal models, diffusion, GANs) and translating them into business use-cases.

Track record of identifying and delivering measurable business impact via ML/AI - e.g., revenue growth, cost savings, improved efficiency.

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.

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).

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.