Staff Machine Learning Engineer

Lead the design and delivery of mission-critical ML systems for Xometry’s AI/ML integration, with emphasis on real-time serving and low-latency data flows into partner tooling.

Responsibilities

• Own end-to-end delivery from requirements gathering through release, ensuring high-quality, on-time outcomes across complex, cross-functional initiatives
• Architect and build partner integration ML, delivering a high-performance AI/ML layer for the embedded DFM AI + IQE integration with Teamcenter and Designcenter
• Design real-time ML serving architecture and a low-latency signal path that returns DFM and pricing feedback directly into the designer environment
• Define data contracts for model inputs and outputs; implement MLOps, governance, and observability for a mission-critical public-marketplace partner integration
• Develop cloud-based production systems including real-time endpoints and MLOps integrated with Xometry’s broader systems and infrastructure
• Handle cross-domain technical challenges by evaluating variable factors and aligning solutions with both business and technical objectives
• Surface opportunity areas, drive new processes and solutions, and build multi-quarter roadmaps for key technical goals
• Apply best practices across automated testing, parallel and distributed computing, and secure software development for ML systems
• Collaborate with engineers, product managers, data scientists, and business stakeholders to translate requirements into robust technical solutions
• Conduct design reviews, code reviews, and provide technical mentorship to raise team capability
• Stay current with ML/AI advances and introduce relevant new approaches, tools, and frameworks

Requirements

• Bachelor’s degree in a STEM field (or equivalent experience) plus 6-8 years of experience in machine learning engineering, with proven ownership of complex production ML systems
• Strong expertise in ML and AI technologies, including Gradient Boosting, Deep Learning, and/or Generative AI frameworks, with emphasis on backend scalability and reusability
• Hands-on experience deploying real-time ML products at scale in cloud environments (with AWS strongly preferred), including auto-scaling, monitoring, and alerting
• Advanced proficiency in Python and ML/AI frameworks such as TensorFlow or PyTorch
• Solid software engineering fundamentals, including data structures and algorithms
• Experience with MLOps: model monitoring, data and concept drift detection, and automated retraining plus redeployment pipelines
• Proficiency with CI/CD pipelines (e.g., GitHub Actions), test-driven development, and infrastructure as code (e.g., Terraform)
• Demonstrated ability to profile and optimize existing ML deployments for latency and throughput
• Capability to operate independently on new and ambiguous assignments, select methods and procedures, and communicate effectively across engineering, product, and business audiences
• Experience with modern modeling approaches including transformers, self-supervised pre-training, large language models (LLMs), or generative AI
• Knowledge of containers, Kubernetes, and cloud-native distributed systems
• Manufacturing, supply chain, or marketplace background is a plus, with emphasis that curiosity and drive matter

Technologies

• Python
• TensorFlow
• PyTorch
• Gradient Boosting
• Deep Learning
• Generative AI frameworks
• AWS
• CI/CD pipelines
• GitHub Actions
• Test-driven development
• Terraform
• MLOps
• Model monitoring
• Data and concept drift detection
• Auto-scaling
• Containers
• Kubernetes
• Transformers
• Self-supervised pre-training
• Large language models (LLMs)
• Solid Edge
• NX
• Designcenter
• Teamcenter
• Parallel and distributed computing

Benefits

• 401(k) match
• Medical, dental and vision insurance
• Life and disability insurance
• Generous paid time off including vacation, sick leave, floating and fixed holidays
• Maternity and bonding leave
• EAP and other wellbeing resources